We introduce RACH-Space, a novel classification method in ensemble learning. In particular, we show its applicability as a label model for weakly supervised learning. RACH-Space offers simplicity in implementation with minimal assumptions on the data or weak signals. The model is well suited for scenarios where fully labeled data is not available. Our method is built upon geometrical interpretation of the space spanned by weak signals. Our analysis of the high dimensional convex hull structure underlying general set of weak signals bridges geometry with machine learning. Empirical results also demonstrate that RACH-Space works well in practice and compares favorably to best existing label models for weakly supervised learning.  ( 2 min )

In this paper, we introduce a novel analysis of neural networks based on geometric (Clifford) algebra and convex optimization. We show that optimal weights of deep ReLU neural networks are given by the wedge product of training samples when trained with standard regularized loss. Furthermore, the training problem reduces to convex optimization over wedge product features, which encode the geometric structure of the training dataset. This structure is given in terms of signed volumes of triangles and parallelotopes generated by data vectors. The convex problem finds a small subset of samples via $\ell_1$ regularization to discover only relevant wedge product features. Our analysis provides a novel perspective on the inner workings of deep neural networks and sheds light on the role of the hidden layers.  ( 2 min )

Property prediction plays an important role in material discovery. As an initial step to eventually develop a foundation model for material science, we introduce a new autoencoder called the MHG-GNN, which combines graph neural network (GNN) with Molecular Hypergraph Grammar (MHG). Results on a variety of property prediction tasks with diverse materials show that MHG-GNN is promising.  ( 2 min )

It can largely benefit the reinforcement learning (RL) process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively. Different from multi-agent reinforcement learning (MARL) where multiple agents are in a common environment and should learn to cooperate or compete with each other, in this case each agent has its separate environment and only communicates with others to share knowledge without any cooperative or competitive behaviour as a learning outcome. In fact, this scenario exists widely in real life whose concept can be utilised in many applications, but is not well understood yet and not well formulated. As the first effort, we propose group-agent system for RL as a formulation of this scenario and the third type of RL system with respect to single-agent and multi-agent systems. We then propose a distributed RL framework called DDAL (Decentralised Distributed Asynchronous Learning) designed for group-agent reinforcement learning (GARL). We show through experiments that DDAL achieved desirable performance with very stable training and has good scalability.  ( 2 min )

Classifiers built upon vision-language models such as CLIP have shown remarkable zero-shot performance across a broad range of image classification tasks. Prior work has studied different ways of automatically creating descriptor sets for every class based on prompt templates, ranging from manually engineered templates over templates obtained from a large language model to templates built from random words and characters. In contrast, deriving zero-shot classifiers from the respective encoded class descriptors has remained nearly unchanged, that is: classify to the class that maximizes the cosine similarity between its averaged encoded class descriptors and the encoded image. However, weighting all class descriptors equally can be suboptimal when certain descriptors match visual clues on a given image better than others. In this work, we propose AutoCLIP, a method for auto-tuning zero-shot classifiers. AutoCLIP assigns to each prompt template per-image weights, which are derived from statistics of class descriptor-image similarities at inference time. AutoCLIP is fully unsupervised, has very low overhead, and can be easily implemented in few lines of code. We show that for a broad range of vision-language models, datasets, and prompt templates, AutoCLIP outperforms baselines consistently and by up to 3 percent point accuracy.  ( 2 min )

Consistency regularization-based methods are prevalent in semi-supervised learning (SSL) algorithms due to their exceptional performance. However, they mainly depend on domain-specific data augmentations, which are not usable in domains where data augmentations are less practicable. On the other hand, Pseudo-labeling (PL) is a general and domain-agnostic SSL approach that, unlike consistency regularization-based methods, does not rely on the domain. PL underperforms due to the erroneous high-confidence predictions from poorly calibrated models. This paper proposes an uncertainty-aware pseudo-label selection framework that employs uncertainty sets yielded by the conformal regularization algorithm to fix the poor calibration neural networks, reducing noisy training data. The codes of this work are available at: https://github.com/matinmoezzi/ups conformal classification  ( 2 min )

Adversarial Machine Learning (AML) is a rapidly growing field of security research, with an often overlooked area being model attacks through side-channels. Previous works show such attacks to be serious threats, though little progress has been made on efficient remediation strategies that avoid costly model re-engineering. This work demonstrates a new defense against AML side-channel attacks using model compilation techniques, namely tensor optimization. We show relative model attack effectiveness decreases of up to 43% using tensor optimization, discuss the implications, and direction of future work.  ( 2 min )

Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Machine learning models, including the now ubiquitous transformers, struggle to generalize in this way, and typically require thousands of examples of a concept during training in order to generalize meaningfully. This difference in ability between humans and artificial neural architectures, motivates this study on a neuro-symbolic architecture called the Compositional Program Generator (CPG). CPG has three key features: modularity, type abstraction, and recursive composition, that enable it to generalize both systematically to new concepts in a few-shot manner, as well as productively by length on various sequence-to-sequence language tasks. For each input, CPG uses a grammar of the input domain and a parser to generate a type hierarchy in which each grammar rule is assigned its own unique semantic module, a probabilistic copy or substitution program. Instances with the same hierarchy are processed with the same composed program, while those with different hierarchies may be processed with different programs. CPG learns parameters for the semantic modules and is able to learn the semantics for new types incrementally. Given a context-free grammar of the input language and a dictionary mapping each word in the source language to its interpretation in the output language, CPG can achieve perfect generalization on the SCAN and COGS benchmarks, in both standard and extreme few-shot settings.  ( 3 min )

Semi-supervised learning (SSL) is a promising approach for training deep classification models using labeled and unlabeled datasets. However, existing SSL methods rely on a large unlabeled dataset, which may not always be available in many real-world applications due to legal constraints (e.g., GDPR). In this paper, we investigate the research question: Can we train SSL models without real unlabeled datasets? Instead of using real unlabeled datasets, we propose an SSL method using synthetic datasets generated from generative foundation models trained on datasets containing millions of samples in diverse domains (e.g., ImageNet). Our main concepts are identifying synthetic samples that emulate unlabeled samples from generative foundation models and training classifiers using these synthetic samples. To achieve this, our method is formulated as an alternating optimization problem: (i) meta-learning of generative foundation models and (ii) SSL of classifiers using real labeled and synthetic unlabeled samples. For (i), we propose a meta-learning objective that optimizes latent variables to generate samples that resemble real labeled samples and minimize the validation loss. For (ii), we propose a simple unsupervised loss function that regularizes the feature extractors of classifiers to maximize the performance improvement obtained from synthetic samples. We confirm that our method outperforms baselines using generative foundation models on SSL. We also demonstrate that our methods outperform SSL using real unlabeled datasets in scenarios with extremely small amounts of labeled datasets. This suggests that synthetic samples have the potential to provide improvement gains more efficiently than real unlabeled data.  ( 3 min )

This work presents an instance-agnostic learning framework that fuses vision with dynamics to simultaneously learn shape, pose trajectories, and physical properties via the use of geometry as a shared representation. Unlike many contact learning approaches that assume motion capture input and a known shape prior for the collision model, our proposed framework learns an object's geometric and dynamic properties from RGBD video, without requiring either category-level or instance-level shape priors. We integrate a vision system, BundleSDF, with a dynamics system, ContactNets, and propose a cyclic training pipeline to use the output from the dynamics module to refine the poses and the geometry from the vision module, using perspective reprojection. Experiments demonstrate our framework's ability to learn the geometry and dynamics of rigid and convex objects and improve upon the current tracking framework.  ( 2 min )

Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher load-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. NMPC offers an effective means to control such robots, but its extensive computational demands often limit its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe approximation of NMPC using imitation learning and a predictive safety filter. Our framework significantly reduces computation time while incurring a slight loss in performance. Compared to NMPC, our framework shows more than a eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation, all while guaranteeing safety constraints. Notably, our approach outperforms conventional reinforcement learning methods. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.  ( 2 min )

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past three years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning-based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinising the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.  ( 3 min )

The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating their knowledge can benefit TinyML applications. Federated meta-learning is a promising answer to this question, as it addresses the scarcity of labeled data and heterogeneous data distribution across devices in the real world. However, deploying TinyML hardware faces unique resource constraints, making existing methods impractical due to energy, privacy, and communication limitations. We introduce TinyMetaFed, a model-agnostic meta-learning framework suitable for TinyML. TinyMetaFed facilitates collaborative training of a neural network initialization that can be quickly fine-tuned on new devices. It offers communication savings and privacy protection through partial local reconstruction and Top-P% selective communication, computational efficiency via online learning, and robustness to client heterogeneity through few-shot learning. The evaluations on three TinyML use cases demonstrate that TinyMetaFed can significantly reduce energy consumption and communication overhead, accelerate convergence, and stabilize the training process.  ( 2 min )

Pairwise comparison of graphs is key to many applications in Machine learning ranging from clustering, kernel-based classification/regression and more recently supervised graph prediction. Distances between graphs usually rely on informative representations of these structured objects such as bag of substructures or other graph embeddings. A recently popular solution consists in representing graphs as metric measure spaces, allowing to successfully leverage Optimal Transport, which provides meaningful distances allowing to compare them: the Gromov-Wasserstein distances. However, this family of distances overlooks edge attributes, which are essential for many structured objects. In this work, we introduce an extension of Gromov-Wasserstein distance for comparing graphs whose both nodes and edges have features. We propose novel algorithms for distance and barycenter computation. We empirically show the effectiveness of the novel distance in learning tasks where graphs occur in either input space or output space, such as classification and graph prediction.  ( 2 min )

In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep Operator Networks (APCONs) designed to address the multiscale time-dependent linear transport problem. We observe that the vanilla physics-informed DeepONets with modified MLP may exhibit instability in maintaining the desired limiting macroscopic behavior. Therefore, this necessitates the utilization of an asymptotic-preserving loss function. Drawing inspiration from the heat kernel in the diffusion equation, we propose a new architecture called Convolutional Deep Operator Networks, which employ multiple local convolution operations instead of a global heat kernel, along with pooling and activation operations in each filter layer. Our APCON methods possess a parameter count that is independent of the grid size and are capable of capturing the diffusive behavior of the linear transport problem. Finally, we validate the effectiveness of our methods through several numerical examples.  ( 2 min )

Automated diagnosis of eczema from digital camera images is crucial for developing applications that allow patients to self-monitor their recovery. An important component of this is the segmentation of eczema region from such images. Current methods for eczema segmentation rely on deep neural networks such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While effective, these methods require high volume of annotated data, which can be difficult to obtain. Here, we investigate the capabilities of visual in-context learning that can perform few-shot eczema segmentation with just a handful of examples and without any need for retraining models. Specifically, we propose a strategy for applying in-context learning for eczema segmentation with a generalist vision model called SegGPT. When benchmarked on a dataset of annotated eczema images, we show that SegGPT with just 2 representative example images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net trained on 428 images (mIoU: 32.60). We also discover that using more number of examples for SegGPT may in fact be harmful to its performance. Our result highlights the importance of visual in-context learning in developing faster and better solutions to skin imaging tasks. Our result also paves the way for developing inclusive solutions that can cater to minorities in the demographics who are typically heavily under-represented in the training data.  ( 2 min )

The recent surge of interest surrounding Multimodal Neural Networks (MM-NN) is attributed to their ability to effectively process and integrate multiscale information from diverse data sources. MM-NNs extract and fuse features from multiple modalities using adequate unimodal backbones and specific fusion networks. Although this helps strengthen the multimodal information representation, designing such networks is labor-intensive. It requires tuning the architectural parameters of the unimodal backbones, choosing the fusing point, and selecting the operations for fusion. Furthermore, multimodality AI is emerging as a cutting-edge option in Internet of Things (IoT) systems where inference latency and energy consumption are critical metrics in addition to accuracy. In this paper, we propose Harmonic-NAS, a framework for the joint optimization of unimodal backbones and multimodal fusion networks with hardware awareness on resource-constrained devices. Harmonic-NAS involves a two-tier optimization approach for the unimodal backbone architectures and fusion strategy and operators. By incorporating the hardware dimension into the optimization, evaluation results on various devices and multimodal datasets have demonstrated the superiority of Harmonic-NAS over state-of-the-art approaches achieving up to 10.9% accuracy improvement, 1.91x latency reduction, and 2.14x energy efficiency gain.  ( 2 min )

This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two ($\text{MTP}_2$). By introducing the concept of bridge, which commonly exists in large-scale sparse graphs, we show that the entire problem can be equivalently optimized through (1) several smaller-scaled sub-problems induced by a \emph{bridge-block decomposition} on the thresholded sample covariance graph and (2) a set of explicit solutions on entries corresponding to \emph{bridges}. From practical aspect, this simple and provable discipline can be applied to break down a large problem into small tractable ones, leading to enormous reduction on the computational complexity and substantial improvements for all existing algorithms. The synthetic and real-world experiments demonstrate that our proposed method presents a significant speed-up compared to the state-of-the-art benchmarks.  ( 2 min )

Regularization plays a crucial role in machine learning models, especially for deep neural networks. The existing regularization techniques mainly rely on the i.i.d. assumption and only consider the knowledge from the current sample, without the leverage of the neighboring relationship between samples. In this work, we propose a general regularizer called \textbf{Patch-level Neighborhood Interpolation~(Pani)} that conducts a non-local representation in the computation of networks. Our proposal explicitly constructs patch-level graphs in different layers and then linearly interpolates neighborhood patch features, serving as a general and effective regularization strategy. Further, we customize our approach into two kinds of popular regularization methods, namely Virtual Adversarial Training (VAT) and MixUp as well as its variants. The first derived \textbf{Pani VAT} presents a novel way to construct non-local adversarial smoothness by employing patch-level interpolated perturbations. The second derived \textbf{Pani MixUp} method extends the MixUp, and achieves superiority over MixUp and competitive performance over state-of-the-art variants of MixUp method with a significant advantage in computational efficiency. Extensive experiments have verified the effectiveness of our Pani approach in both supervised and semi-supervised settings.  ( 2 min )

Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud. Different from traditional federated learning, DFL leverages multiple stochastic gradient descent iterations on local datasets within each global aggregation period and intermittently aggregates model parameters through edge servers in local subnetworks. During global synchronization, the cloud server consolidates local models with the outdated global model using a local-global combiner, thus preserving crucial elements of both, enhancing learning efficiency under the presence of delay. A set of conditions is obtained to achieve the sub-linear convergence rate of O(1/k) for strongly convex and smooth loss functions. Based on these findings, an adaptive control algorithm is developed for DFL, implementing policies to mitigate energy consumption and communication latency while aiming for sublinear convergence. Numerical evaluations show DFL's superior performance in terms of faster global model convergence, reduced resource consumption, and robustness against communication delays compared to existing FL algorithms. In summary, this proposed method offers improved efficiency and results when dealing with both convex and non-convex loss functions.  ( 2 min )

As machine learning models are increasingly being employed in various high-stakes settings, it becomes important to ensure that predictions of these models are not only adversarially robust, but also readily explainable to relevant stakeholders. However, it is unclear if these two notions can be simultaneously achieved or if there exist trade-offs between them. In this work, we make one of the first attempts at studying the impact of adversarially robust models on actionable explanations which provide end users with a means for recourse. We theoretically and empirically analyze the cost (ease of implementation) and validity (probability of obtaining a positive model prediction) of recourses output by state-of-the-art algorithms when the underlying models are adversarially robust vs. non-robust. More specifically, we derive theoretical bounds on the differences between the cost and the validity of the recourses generated by state-of-the-art algorithms for adversarially robust vs. non-robust linear and non-linear models. Our empirical results with multiple real-world datasets validate our theoretical results and show the impact of varying degrees of model robustness on the cost and validity of the resulting recourses. Our analyses demonstrate that adversarially robust models significantly increase the cost and reduce the validity of the resulting recourses, thus shedding light on the inherent trade-offs between adversarial robustness and actionable explanations  ( 2 min )

While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive scientific measurements. Machine learning is becoming an appealing alternative as sophisticated predictive techniques are being used to quickly and cheaply produce large amounts of predicted labels; e.g., predicted protein structures are used to supplement experimentally derived structures, predictions of socioeconomic indicators from satellite imagery are used to supplement accurate survey data, and so on. Since predictions are imperfect and potentially biased, this practice brings into question the validity of downstream inferences. We introduce cross-prediction: a method for valid inference powered by machine learning. With a small labeled dataset and a large unlabeled dataset, cross-prediction imputes the missing labels via machine learning and applies a form of debiasing to remedy the prediction inaccuracies. The resulting inferences achieve the desired error probability and are more powerful than those that only leverage the labeled data. Closely related is the recent proposal of prediction-powered inference, which assumes that a good pre-trained model is already available. We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model. Finally, we observe that cross-prediction gives more stable conclusions than its competitors; its confidence intervals typically have significantly lower variability.  ( 2 min )

Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data. Model heterogeneity and catastrophic forgetting are two crucial challenges, which greatly limit the applicability and generalizability. This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation, facilitating the both intra-domain discriminability and inter-domain generalization. For heterogeneity issue, we leverage irrelevant unlabeled public data for communication between the heterogeneous participants. We construct cross-correlation matrix and align instance similarity distribution on both logits and feature levels, which effectively overcomes the communication barrier and improves the generalizable ability. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation, which retains inter-domain knowledge while avoiding the optimization conflict issue, fulling distilling privileged inter-domain information through depicting posterior classes relation. Considering that there is no standard benchmark for evaluating existing heterogeneous federated learning under the same setting, we present a comprehensive benchmark with extensive representative methods under four domain shift scenarios, supporting both heterogeneous and homogeneous federated settings. Empirical results demonstrate the superiority of our method and the efficiency of modules on various scenarios.  ( 2 min )

The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery.  ( 2 min )

We present a method for synthesizing dynamic, reduced-order output-feedback polynomial control policies for control-affine nonlinear systems which guarantees runtime stability to a goal state, when using visual observations and a learned perception module in the feedback control loop. We leverage Lyapunov analysis to formulate the problem of synthesizing such policies. This problem is nonconvex in the policy parameters and the Lyapunov function that is used to prove the stability of the policy. To solve this problem approximately, we propose two approaches: the first solves a sequence of sum-of-squares optimization problems to iteratively improve a policy which is provably-stable by construction, while the second directly performs gradient-based optimization on the parameters of the polynomial policy, and its closed-loop stability is verified a posteriori. We extend our approach to provide stability guarantees in the presence of observation noise, which realistically arises due to errors in the learned perception module. We evaluate our approach on several underactuated nonlinear systems, including pendula and quadrotors, showing that our guarantees translate to empirical stability when controlling these systems from images, while baseline approaches can fail to reliably stabilize the system.

Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memory resources. In this work, we show how to leverage randomness and design models that are both expressive and invariant but use less resources. Inspired by randomized algorithms, our key insight is that accepting probabilistic notions of universal approximation and invariance can reduce our resource requirements. More specifically, we propose a class of binary classification models called Randomized Linear Classifiers (RLCs). We give parameter and sample size conditions in which RLCs can, with high probability, approximate any (smooth) function while preserving invariance to compact group transformations. Leveraging this result, we design three RLCs that are provably probabilistic invariant for classification tasks over sets, graphs, and spherical data. We show how these models can achieve probabilistic invariance and universality using less resources than (deterministic) neural networks and their invariant counterparts. Finally, we empirically demonstrate the benefits of this new class of models on invariant tasks where deterministic invariant neural networks are known to struggle.

Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a novel method to alleviate these problems that we call odd-$k$-out learning (OKO), which minimizes the cross-entropy error for sets rather than for single examples. This naturally allows the model to capture correlations across data examples and achieves both better accuracy and calibration, especially in limited training data and class-imbalanced regimes. Perhaps surprisingly, OKO often yields better calibration even when training with hard labels and dropping any additional calibration parameter tuning, such as temperature scaling. We provide theoretical justification, establishing that OKO naturally yields better calibration, and provide extensive experimental analyses that corroborate our theoretical findings. We emphasize that OKO is a general framework that can be easily adapted to many settings and the trained model can be applied to single examples at inference time, without introducing significant run-time overhead or architecture changes.

The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time. Whilst mostly investigated in the context of selected benchmark data sets in the area of computer vision, we explore this aspect for the audio scene classification task of the DCASE2020 challenge data. Our analysis is based on twodimensional filter-normalised visualisations and a derived sharpness measure. Our exploratory analysis shows that sharper minima tend to show better generalisation than flat minima -even more so for out-of-domain data, recorded from previously unseen devices-, thus adding to the dispute about better generalisation capabilities of flat minima. We further find that, in particular, the choice of optimisers is a main driver of the sharpness of minima and we discuss resulting limitations with respect to comparability. Our code, trained model states and loss landscape visualisations are publicly available.

Contrastive deep graph clustering (CDGC) utilizes contrastive learning to group nodes into different clusters. Better augmentation techniques benefit the quality of the contrastive samples, thus being one of key factors to improve performance. However, the augmentation samples in existing methods are always predefined by human experiences, and agnostic from the downstream task clustering, thus leading to high human resource costs and poor performance. To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC. Specifically, we design two learnable augmentors for attribute and structure information, respectively. Besides, two refinement matrices, including the high-confidence pseudo-label matrix and the cross-view sample similarity matrix, are generated to improve the reliability of the learned affinity matrix. During the training procedure, we notice that there exist differences between the optimization goals for training learnable augmentors and contrastive learning networks. In other words, we should both guarantee the consistency of the embeddings as well as the diversity of the augmented samples. Thus, an adversarial learning mechanism is designed in our method. Moreover, a two-stage training strategy is leveraged for the high-confidence refinement matrices. Extensive experimental results demonstrate the effectiveness of AGCLA on six benchmark datasets.

Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at https://anonymous.4open.science/r/code-2022-dynabench/.

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy. We provide an exhaustive categorization for VFL settings and privacy-preserving protocols and comprehensively analyze the privacy attacks and defense strategies for each protocol. In the end, we propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, as well as effectiveness and fairness constraints. Finally, we review the most recent advances in industrial applications, highlighting open challenges and future directions for VFL.

The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which makes it hard to analyze and compare different data filtering approaches. DataComp is a new benchmark dedicated to evaluating different methods for data filtering. This paper describes our learning and solution when participating in the DataComp challenge. Our filtering strategy includes three stages: single-modality filtering, cross-modality filtering, and data distribution alignment. We integrate existing methods and propose new solutions, such as computing CLIP score on horizontally flipped images to mitigate the interference of scene text, using vision and language models to retrieve training samples for target downstream tasks, rebalancing the data distribution to improve the efficiency of allocating the computational budget, etc. We slice and dice our design choices, provide in-depth analysis, and discuss open questions. Our approach outperforms the best method from the DataComp paper by over 4% on the average performance of 38 tasks and by over 2% on ImageNet.

This study explored the potential of gait analysis as a tool for assessing post-injury complications, e.g., infection, malunion, or hardware irritation, in patients with lower extremity fractures. The research focused on the proficiency of supervised machine learning models predicting complications using consecutive gait datasets. We identified patients with lower extremity fractures at an academic center. Patients underwent gait analysis with a chest-mounted IMU device. Using software, raw gait data was preprocessed, emphasizing 12 essential gait variables. Machine learning models including XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained, tested, and evaluated. Attention was given to class imbalance, addressed using SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait variables, independent of the time difference between gait analyses. XGBoost was the optimal model both before and after applying SMOTE. Prior to SMOTE, the model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed importance to the duration between injury and gait analysis. Data patterns showed early physiological compensations, followed by stabilization phases, emphasizing prompt gait analysis. This study underscores the potential of machine learning, particularly XGBoost, in gait analysis for orthopedic care. Predicting post-injury complications, early gait assessment becomes vital, revealing intervention points. The findings support a shift in orthopedics towards a data-informed approach, enhancing patient outcomes.

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalities act as detectors in the information pathway. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of downstream tasks. Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks.

Graph unlearning has emerged as a pivotal method to delete information from a pre-trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges. An unlearning method enables the GNN model to comply with data protection regulations (i.e., the right to be forgotten), adapt to evolving data distributions, and reduce the GPU-hours carbon footprint by avoiding repetitive retraining. Existing partitioning and aggregation-based methods have limitations due to their poor handling of local graph dependencies and additional overhead costs. More recently, GNNDelete offered a model-agnostic approach that alleviates some of these issues. Our work takes a novel approach to address these challenges in graph unlearning through knowledge distillation, as it distills to delete in GNN (D2DGN). It is a model-agnostic distillation framework where the complete graph knowledge is divided and marked for retention and deletion. It performs distillation with response-based soft targets and feature-based node embedding while minimizing KL divergence. The unlearned model effectively removes the influence of deleted graph elements while preserving knowledge about the retained graph elements. D2DGN surpasses the performance of existing methods when evaluated on various real-world graph datasets by up to $43.1\%$ (AUC) in edge and node unlearning tasks. Other notable advantages include better efficiency, better performance in removing target elements, preservation of performance for the retained elements, and zero overhead costs. Notably, our D2DGN surpasses the state-of-the-art GNNDelete in AUC by $2.4\%$, improves membership inference ratio by $+1.3$, requires $10.2\times10^6$ fewer FLOPs per forward pass and up to $\mathbf{3.2}\times$ faster.

This paper studies Large Language Models (LLMs) for structured data--particularly graphs--a crucial data modality that remains underexplored in the LLM literature. We aim to understand when and why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs on node classification tasks. To address the ``when'' question, we examine a variety of prompting methods for encoding structural information, in settings where textual node features are either rich or scarce. For the ``why'' questions, we probe into two potential contributing factors to the LLM performance: data leakage and homophily. Our exploration of these questions reveals that (i) LLMs can benefit from structural information, especially when textual node features are scarce; (ii) there is no substantial evidence indicating that the performance of LLMs is significantly attributed to data leakage; and (iii) the performance of LLMs on a target node is strongly positively related to the local homophily ratio of the node.

Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard.

The primary bottleneck towards obtaining good recognition performance in IR images is the lack of sufficient labeled training data, owing to the cost of acquiring such data. Realizing that object detection methods for the RGB modality are quite robust (at least for some commonplace classes, like person, car, etc.), thanks to the giant training sets that exist, in this work we seek to leverage cues from the RGB modality to scale object detectors to the IR modality, while preserving model performance in the RGB modality. At the core of our method, is a novel tensor decomposition method called TensorFact which splits the convolution kernels of a layer of a Convolutional Neural Network (CNN) into low-rank factor matrices, with fewer parameters than the original CNN. We first pretrain these factor matrices on the RGB modality, for which plenty of training data are assumed to exist and then augment only a few trainable parameters for training on the IR modality to avoid over-fitting, while encouraging them to capture complementary cues from those trained only on the RGB modality. We validate our approach empirically by first assessing how well our TensorFact decomposed network performs at the task of detecting objects in RGB images vis-a-vis the original network and then look at how well it adapts to IR images of the FLIR ADAS v1 dataset. For the latter, we train models under scenarios that pose challenges stemming from data paucity. From the experiments, we observe that: (i) TensorFact shows performance gains on RGB images; (ii) further, this pre-trained model, when fine-tuned, outperforms a standard state-of-the-art object detector on the FLIR ADAS v1 dataset by about 4% in terms of mAP 50 score.

Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained by deep neural networks has demonstrated superior performance, whereby representations containing no demographic information are inferred from the data and then used as the input to classification or other downstream tasks. Despite the development of FRL methods, their vulnerability under data poisoning attack, a popular protocol to benchmark model robustness under adversarial scenarios, is under-explored. Data poisoning attacks have been developed for classical fair machine learning methods which incorporate fairness constraints into shallow-model classifiers. Nonetheless, these attacks fall short in FRL due to notably different fairness goals and model architectures. This work proposes the first data poisoning framework attacking FRL. We induce the model to output unfair representations that contain as much demographic information as possible by injecting carefully crafted poisoning samples into the training data. This attack entails a prohibitive bilevel optimization, wherefore an effective approximated solution is proposed. A theoretical analysis on the needed number of poisoning samples is derived and sheds light on defending against the attack. Experiments on benchmark fairness datasets and state-of-the-art fair representation learning models demonstrate the superiority of our attack.

We consider the problem of private distributed multi-party multiplication. It is well-established that Shamir secret-sharing coding strategies can enable perfect information-theoretic privacy in distributed computation via the celebrated algorithm of Ben Or, Goldwasser and Wigderson (the "BGW algorithm"). However, perfect privacy and accuracy require an honest majority, that is, $N \geq 2t+1$ compute nodes are required to ensure privacy against any $t$ colluding adversarial nodes. By allowing for some controlled amount of information leakage and approximate multiplication instead of exact multiplication, we study coding schemes for the setting where the number of honest nodes can be a minority, that is $N< 2t+1.$ We develop a tight characterization privacy-accuracy trade-off for cases where $N < 2t+1$ by measuring information leakage using {differential} privacy instead of perfect privacy, and using the mean squared error metric for accuracy. A novel technical aspect is an intricately layered noise distribution that merges ideas from differential privacy and Shamir secret-sharing at different layers.  ( 2 min )

The purpose of this work is to transport the information from multiple randomized controlled trials to the target population where we only have the control group data. Previous works rely critically on the mean exchangeability assumption. However, as pointed out by many current studies, the mean exchangeability assumption might be violated. Motivated by the synthetic control method, we construct a synthetic treatment group for the target population by a weighted mixture of treatment groups of source populations. We estimate the weights by minimizing the conditional maximum mean discrepancy between the weighted control groups of source populations and the target population. We establish the asymptotic normality of the synthetic treatment group estimator based on the sieve semiparametric theory. Our method can serve as a novel complementary approach when the mean exchangeability assumption is violated. Experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of our methods.  ( 2 min )

Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. To address this gap, we propose a comprehensive evaluation benchmark LawBench. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize needed legal concepts, articles and facts; (2) Legal knowledge understanding: whether LLMs can comprehend entities, events and relationships within legal text; (3) Legal knowledge applying: whether LLMs can properly utilize their legal knowledge and make necessary reasoning steps to solve realistic legal tasks. LawBench contains 20 diverse tasks covering 5 task types: single-label classification (SLC), multi-label classification (MLC), regression, extraction and generation. We perform extensive evaluations of 51 LLMs on LawBench, including 20 multilingual LLMs, 22 Chinese-oriented LLMs and 9 legal specific LLMs. The results show that GPT-4 remains the best-performing LLM in the legal domain, surpassing the others by a significant margin. While fine-tuning LLMs on legal specific text brings certain improvements, we are still a long way from obtaining usable and reliable LLMs in legal tasks. All data, model predictions and evaluation code are released in https://github.com/open-compass/LawBench/. We hope this benchmark provides in-depth understanding of the LLMs' domain-specified capabilities and speed up the development of LLMs in the legal domain.  ( 3 min )

Batch reinforcement learning (RL) defines the task of learning from a fixed batch of data lacking exhaustive exploration. Worst-case optimality algorithms, which calibrate a value-function model class from logged experience and perform some type of pessimistic evaluation under the learned model, have emerged as a promising paradigm for batch RL. However, contemporary works on this stream have commonly overlooked the hierarchical decision-making structure hidden in the optimization landscape. In this paper, we adopt a game-theoretical viewpoint and model the policy learning diagram as a two-player general-sum game with a leader-follower structure. We propose a novel stochastic gradient-based learning algorithm: StackelbergLearner, in which the leader player updates according to the total derivative of its objective instead of the usual individual gradient, and the follower player makes individual updates and ensures transition-consistent pessimistic reasoning. The derived learning dynamic naturally lends StackelbergLearner to a game-theoretic interpretation and provides a convergence guarantee to differentiable Stackelberg equilibria. From a theoretical standpoint, we provide instance-dependent regret bounds with general function approximation, which shows that our algorithm can learn a best-effort policy that is able to compete against any comparator policy that is covered by batch data. Notably, our theoretical regret guarantees only require realizability without any data coverage and strong function approximation conditions, e.g., Bellman closedness, which is in contrast to prior works lacking such guarantees. Through comprehensive experiments, we find that our algorithm consistently performs as well or better as compared to state-of-the-art methods in batch RL benchmark and real-world datasets.  ( 2 min )

We study unconstrained Online Linear Optimization with Lipschitz losses. The goal is to simultaneously achieve ($i$) second order gradient adaptivity; and ($ii$) comparator norm adaptivity also known as "parameter freeness" in the literature. Existing regret bounds (Cutkosky and Orabona, 2018; Mhammedi and Koolen, 2020; Jacobsen and Cutkosky, 2022) have the suboptimal $O(\sqrt{V_T\log V_T})$ dependence on the gradient variance $V_T$, while the present work improves it to the optimal rate $O(\sqrt{V_T})$ using a novel continuous-time-inspired algorithm, without any impractical doubling trick. This result can be extended to the setting with unknown Lipschitz constant, eliminating the range ratio problem from prior works (Mhammedi and Koolen, 2020). Concretely, we first show that the aimed simultaneous adaptivity can be achieved fairly easily in a continuous time analogue of the problem, where the environment is modeled by an arbitrary continuous semimartingale. Then, our key innovation is a new discretization argument that preserves such adaptivity in the discrete time adversarial setting. This refines a non-gradient-adaptive discretization argument from (Harvey et al., 2023), both algorithmically and analytically, which could be of independent interest.  ( 2 min )

Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research. Prior results have shown that deep vision models can capture biological signal better than hand-crafted features. This work explores how weakly supervised and self-supervised deep learning approaches scale when training larger models on larger datasets. Our results show that both CNN- and ViT-based masked autoencoders significantly outperform weakly supervised models. At the high-end of our scale, a ViT-L/8 trained on over 3.5-billion unique crops sampled from 95-million microscopy images achieves relative improvements as high as 28% over our best weakly supervised models at inferring known biological relationships curated from public databases.  ( 2 min )

The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance. Nonetheless, traditional approaches have shortcomings when it comes to computational complexity and their ability to adapt to dynamic conditions, creating a gap between theoretical analysis and the practical execution of algorithmic solutions for managing wireless resources. Deep learning-based techniques offer promising solutions for bridging this gap with their substantial representation capabilities. We propose a novel unsupervised deep learning framework, which is called NNBF, for the design of uplink receive multi-user single input multiple output (MU-SIMO) beamforming. The primary objective is to enhance the throughput by focusing on maximizing the sum-rate while also offering computationally efficient solution, in contrast to established conventional methods. We conduct experiments for several antenna configurations. Our experimental results demonstrate that NNBF exhibits superior performance compared to our baseline methods, namely, zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) equalizer. Additionally, NNBF is scalable to the number of single-antenna user equipments (UEs) while baseline methods have significant computational burden due to matrix pseudo-inverse operation.

Light curves of stars encapsulate a wealth of information about stellar oscillations and granulation, thereby offering key insights into the internal structure and evolutionary state of stars. Conventional asteroseismic techniques have been largely confined to power spectral analysis, neglecting the valuable phase information contained within light curves. While recent machine learning applications in asteroseismology utilizing Convolutional Neural Networks (CNNs) have successfully inferred stellar attributes from light curves, they are often limited by the local feature extraction inherent in convolutional operations. To circumvent these constraints, we present $\textit{Astroconformer}$, a Transformer-based deep learning framework designed to capture long-range dependencies in stellar light curves. Our empirical analysis, which focuses on estimating surface gravity ($\log g$), is grounded in a carefully curated dataset derived from $\textit{Kepler}$ light curves. These light curves feature asteroseismic $\log g$ values spanning from 0.2 to 4.4. Our results underscore that, in the regime where the training data is abundant, $\textit{Astroconformer}$ attains a root-mean-square-error (RMSE) of 0.017 dex around $\log g \approx 3 $. Even in regions where training data are sparse, the RMSE can reach 0.1 dex. It outperforms not only the K-nearest neighbor-based model ($\textit{The SWAN}$) but also state-of-the-art CNNs. Ablation studies confirm that the efficacy of the models in this particular task is strongly influenced by the size of their receptive fields, with larger receptive fields correlating with enhanced performance. Moreover, we find that the attention mechanisms within $\textit{Astroconformer}$ are well-aligned with the inherent characteristics of stellar oscillations and granulation present in the light curves.

Mechanistic interpretability seeks to understand the internal mechanisms of machine learning models, where localization -- identifying the important model components -- is a key step. Activation patching, also known as causal tracing or interchange intervention, is a standard technique for this task (Vig et al., 2020), but the literature contains many variants with little consensus on the choice of hyperparameters or methodology. In this work, we systematically examine the impact of methodological details in activation patching, including evaluation metrics and corruption methods. In several settings of localization and circuit discovery in language models, we find that varying these hyperparameters could lead to disparate interpretability results. Backed by empirical observations, we give conceptual arguments for why certain metrics or methods may be preferred. Finally, we provide recommendations for the best practices of activation patching going forwards.

Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. This task has considerable challenges due to immense variation in geographic landscapes. The image-to-image retrieval-based approaches fail to solve this problem on a global scale as it is not feasible to construct a large gallery of images covering the entire world. Instead, existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. However, their performance is limited by the predefined classes and often results in inaccurate localizations when an image's location significantly deviates from its class center. To overcome these limitations, we propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations. GeoCLIP's location encoder models the Earth as a continuous function by employing positional encoding through random Fourier features and constructing a hierarchical representation that captures information at varying resolutions to yield a semantically rich high-dimensional feature suitable to use even beyond geo-localization. To the best of our knowledge, this is the first work employing GPS encoding for geo-localization. We demonstrate the efficacy of our method via extensive experiments and ablations on benchmark datasets. We achieve competitive performance with just 20% of training data, highlighting its effectiveness even in limited-data settings. Furthermore, we qualitatively demonstrate geo-localization using a text query by leveraging CLIP backbone of our image encoder.

Auxiliary data sources have become increasingly important in epidemiological surveillance, as they are often available at a finer spatial and temporal resolution, larger coverage, and lower latency than traditional surveillance signals. We describe the problem of spatial and temporal heterogeneity in these signals derived from these data sources, where spatial and/or temporal biases are present. We present a method to use a ``guiding'' signal to correct for these biases and produce a more reliable signal that can be used for modeling and forecasting. The method assumes that the heterogeneity can be approximated by a low-rank matrix and that the temporal heterogeneity is smooth over time. We also present a hyperparameter selection algorithm to choose the parameters representing the matrix rank and degree of temporal smoothness of the corrections. In the absence of ground truth, we use maps and plots to argue that this method does indeed reduce heterogeneity. Reducing heterogeneity from auxiliary data sources greatly increases their utility in modeling and forecasting epidemics.

We investigate the high-dimensional properties of robust regression estimators in the presence of heavy-tailed contamination of both the covariates and response functions. In particular, we provide a sharp asymptotic characterisation of M-estimators trained on a family of elliptical covariate and noise data distributions including cases where second and higher moments do not exist. We show that, despite being consistent, the Huber loss with optimally tuned location parameter $\delta$ is suboptimal in the high-dimensional regime in the presence of heavy-tailed noise, highlighting the necessity of further regularisation to achieve optimal performance. This result also uncovers the existence of a curious transition in $\delta$ as a function of the sample complexity and contamination. Moreover, we derive the decay rates for the excess risk of ridge regression. We show that, while it is both optimal and universal for noise distributions with finite second moment, its decay rate can be considerably faster when the covariates' second moment does not exist. Finally, we show that our formulas readily generalise to a richer family of models and data distributions, such as generalised linear estimation with arbitrary convex regularisation trained on mixture models.

Rapidly developing machine learning methods has stimulated research interest in computationally reconstructing differential equations (DEs) from observational data which may provide additional insight into underlying causative mechanisms. In this paper, we propose a novel neural-ODE based method that uses spectral expansions in space to learn spatiotemporal DEs. The major advantage of our spectral neural DE learning approach is that it does not rely on spatial discretization, thus allowing the target spatiotemporal equations to contain long range, nonlocal spatial interactions that act on unbounded spatial domains. Our spectral approach is shown to be as accurate as some of the latest machine learning approaches for learning PDEs operating on bounded domains. By developing a spectral framework for learning both PDEs and integro-differential equations, we extend machine learning methods to apply to unbounded DEs and a larger class of problems.

Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applications because they can effectively bypass defense mechanisms. However, previous attack methods often utilize Generative Adversarial Networks (GANs), which are not theoretically provable and thus generate unrealistic examples by incorporating adversarial objectives, especially for large-scale datasets like ImageNet. In this paper, we propose a new method, called AdvDiff, to generate unrestricted adversarial examples with diffusion models. We design two novel adversarial guidance techniques to conduct adversarial sampling in the reverse generation process of diffusion models. These two techniques are effective and stable to generate high-quality, realistic adversarial examples by integrating gradients of the target classifier interpretably. Experimental results on MNIST and ImageNet datasets demonstrate that AdvDiff is effective to generate unrestricted adversarial examples, which outperforms GAN-based methods in terms of attack performance and generation quality.

The susceptibility of modern machine learning classifiers to adversarial examples has motivated theoretical results suggesting that these might be unavoidable. However, these results can be too general to be applicable to natural data distributions. Indeed, humans are quite robust for tasks involving vision. This apparent conflict motivates a deeper dive into the question: Are adversarial examples truly unavoidable? In this work, we theoretically demonstrate that a key property of the data distribution -- concentration on small-volume subsets of the input space -- determines whether a robust classifier exists. We further demonstrate that, for a data distribution concentrated on a union of low-dimensional linear subspaces, exploiting data structure naturally leads to classifiers that enjoy good robustness guarantees, improving upon methods for provable certification in certain regimes.

Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted environments. Recent advancements in learning from demonstrations have shown promising results for robot learning in complex environments. While imitation learning of expert policies has been well-explored, the study of learning expert reward functions is largely under-explored in legged locomotion. This paper brings state-of-the-art Inverse Reinforcement Learning (IRL) techniques to solving bipedal locomotion problems over complex terrains. We propose algorithms for learning expert reward functions, and we subsequently analyze the learned functions. Through nonlinear function approximation, we uncover meaningful insights into the expert's locomotion strategies. Furthermore, we empirically demonstrate that training a bipedal locomotion policy with the inferred reward functions enhances its walking performance on unseen terrains, highlighting the adaptability offered by reward learning.

The Radio Neutrino Observatory in Greenland (RNO-G) is a radio-based ultra-high energy neutrino detector located at Summit Station, Greenland. It is still being constructed, with 7 stations currently operational. Neutrino detection works by measuring Askaryan radiation produced by neutrino-nucleon interactions. A neutrino candidate must be found amidst other backgrounds which are recorded at much higher rates -- including cosmic-rays and anthropogenic noise -- the origins of which are sometimes unknown. Here we describe a method to classify different noise classes using the latent space of a variational autoencoder. The latent space forms a compact representation that makes classification tractable. We analyze data from a noisy and a silent station. The method automatically detects and allows us to qualitatively separate multiple event classes, including physical wind-induced signals, for both the noisy and the quiet station.

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.

We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse.

Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-based model by predicting the latent representation of a target signal $y$ from a context signal $x$. JEPAs bypass the need for data augmentation and negative samples, which are typically required by contrastive learning, while avoiding the overfitting issues associated with generative-based pretraining. In this paper, we show that graph-level representations can be effectively modeled using this paradigm and propose Graph-JEPA, the first JEPA for the graph domain. In particular, we employ masked modeling to learn embeddings for different subgraphs of the input graph. To endow the representations with the implicit hierarchy that is often present in graph-level concepts, we devise an alternative training objective that consists of predicting the coordinates of the encoded subgraphs on the unit hyperbola in the 2D plane. Extensive validation shows that Graph-JEPA can learn representations that are expressive and competitive in both graph classification and regression problems.

Developing the next generation of household robot helpers requires combining locomotion and interaction capabilities, which is generally referred to as mobile manipulation (MoMa). MoMa tasks are difficult due to the large action space of the robot and the common multi-objective nature of the task, e.g., efficiently reaching a goal while avoiding obstacles. Current approaches often segregate tasks into navigation without manipulation and stationary manipulation without locomotion by manually matching parts of the action space to MoMa sub-objectives (e.g. learning base actions for locomotion objectives and learning arm actions for manipulation). This solution prevents simultaneous combinations of locomotion and interaction degrees of freedom and requires human domain knowledge for both partitioning the action space and matching the action parts to the sub-objectives. In this paper, we introduce Causal MoMa, a new reinforcement learning framework to train policies for typical MoMa tasks that makes use of the most favorable subspace of the robot's action space to address each sub-objective. Causal MoMa automatically discovers the causal dependencies between actions and terms of the reward function and exploits these dependencies through causal policy gradient that reduces gradient variance compared to previous state-of-the-art reinforcement learning algorithms, improving convergence and results. We evaluate the performance of Causal MoMa on three types of simulated robots across different MoMa tasks and demonstrate success in transferring the policies trained in simulation directly to a real robot, where our agent is able to follow moving goals and react to dynamic obstacles while simultaneously and synergistically controlling the whole-body: base, arm, and head. More information at https://sites.google.com/view/causal-moma.

A typical machine learning (ML) development cycle for edge computing is to maximise the performance during model training and then minimise the memory/area footprint of the trained model for deployment on edge devices targeting CPUs, GPUs, microcontrollers, or custom hardware accelerators. This paper proposes a methodology for automatically generating predictor circuits for classification of tabular data with comparable prediction performance to conventional ML techniques while using substantially fewer hardware resources and power. The proposed methodology uses an evolutionary algorithm to search over the space of logic gates and automatically generates a classifier circuit with maximised training prediction accuracy. Classifier circuits are so tiny (i.e., consisting of no more than 300 logic gates) that they are called "Tiny Classifier" circuits, and can efficiently be implemented in ASIC or on an FPGA. We empirically evaluate the automatic Tiny Classifier circuit generation methodology or "Auto Tiny Classifiers" on a wide range of tabular datasets, and compare it against conventional ML techniques such as Amazon's AutoGluon, Google's TabNet and a neural search over Multi-Layer Perceptrons. Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline. When synthesised as a Silicon chip, Tiny Classifiers use 8-18x less area and 4-8x less power. When implemented as an ultra-low cost chip on a flexible substrate (i.e., FlexIC), they occupy 10-75x less area and consume 13-75x less power compared to the most hardware-efficient ML baseline. On an FPGA, Tiny Classifiers consume 3-11x fewer resources.

We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuotactile and proprioceptive sensory inputs. These multimodal inputs are fused via a visuotactile transformer, enabling online inference of object shapes and physical properties during deployment. We show significant performance improvements over prior methods and the importance of visual and tactile sensing.

Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability of pellet placements, and existing methods for mapping anatomical landmarks in the X-ray Microbeam Dataset (XRMB) fail to capture the entire anatomy of the vocal tract. In this paper, we propose a new geometric transformation that improves the accuracy of these measurements. Our transformation maps anatomical landmarks' X-Y coordinates along the midsagittal plane onto six relative measures: Lip Aperture (LA), Lip Protusion (LP), Tongue Body Constriction Location (TTCL), Degree (TBCD), Tongue Tip Constriction Location (TTCL) and Degree (TTCD). Our novel contribution is the extension of the palate trace towards the inferred anterior pharyngeal line, which improves measurements of tongue body constriction.

We prove a fundamental limitation on the efficiency of a wide class of Reinforcement Learning (RL) algorithms. This limitation applies to model-free RL methods as well as a broad range of model-based methods, such as planning with tree search. Under an abstract definition of this class, we provide a family of RL problems for which these methods suffer a lower bound exponential in the horizon for their interactions with the environment to find an optimal behavior. However, there exists a method, not tailored to this specific family of problems, which can efficiently solve the problems in the family. In contrast, our limitation does not apply to several types of methods proposed in the literature, for instance, goal-conditioned methods or other algorithms that construct an inverse dynamics model.  ( 2 min )

Contrastive learning is a self-supervised representation learning framework, where two positive views generated through data augmentation are made similar by an attraction force in a data representation space, while a repulsive force makes them far from negative examples. Non-contrastive learning, represented by BYOL and SimSiam, further gets rid of negative examples and improves computational efficiency. While learned representations may collapse into a single point due to the lack of the repulsive force at first sight, Tian et al. (2021) revealed through the learning dynamics analysis that the representations can avoid collapse if data augmentation is sufficiently stronger than regularization. However, their analysis does not take into account commonly-used feature normalization, a normalizer before measuring the similarity of representations, and hence excessively strong regularization may collapse the dynamics, which is an unnatural behavior under the presence of feature normalization. Therefore, we extend the previous theory based on the L2 loss by considering the cosine loss, which involves feature normalization. We show that the cosine loss induces sixth-order dynamics (while the L2 loss induces a third-order one), in which a stable equilibrium dynamically emerges even if there are only collapsed solutions with given initial parameters. Thus, we offer a new understanding that feature normalization plays an important role in robustly preventing the dynamics collapse.

The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation. To obtain higher quality solutions, Lattanzi and Sohler (ICML 2019) proposed augmenting $k$-means++ with $O(k \log \log k)$ local search steps obtained through the $k$-means++ sampling distribution to yield a $c$-approximation to the $k$-means clustering problem, where $c$ is a large absolute constant. Here we generalize and extend their local search algorithm by considering larger and more sophisticated local search neighborhoods hence allowing to swap multiple centers at the same time. Our algorithm achieves a $9 + \varepsilon$ approximation ratio, which is the best possible for local search. Importantly we show that our approach yields substantial practical improvements, we show significant quality improvements over the approach of Lattanzi and Sohler (ICML 2019) on several datasets.

We introduce the modified planar rotator method (MPRS), a physically inspired machine learning method for spatial/temporal regression. MPRS is a non-parametric model which incorporates spatial or temporal correlations via short-range, distance-dependent ``interactions'' without assuming a specific form for the underlying probability distribution. Predictions are obtained by means of a fully autonomous learning algorithm which employs equilibrium conditional Monte Carlo simulations. MPRS is able to handle scattered data and arbitrary spatial dimensions. We report tests on various synthetic and real-word data in one, two and three dimensions which demonstrate that the MPRS prediction performance (without parameter tuning) is competitive with standard interpolation methods such as ordinary kriging and inverse distance weighting. In particular, MPRS is a particularly effective gap-filling method for rough and non-Gaussian data (e.g., daily precipitation time series). MPRS shows superior computational efficiency and scalability for large samples. Massive data sets involving millions of nodes can be processed in a few seconds on a standard personal computer.

Parameter inference for dynamical models of (bio)physical systems remains a challenging problem. Intractable gradients, high-dimensional spaces, and non-linear model functions are typically problematic without large computational budgets. A recent body of work in that area has focused on Bayesian inference methods, which consider parameters under their statistical distributions and therefore, do not derive point estimates of optimal parameter values. Here we propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations (DR-FFIT) to address these bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. We use artificial neural networks to obtain differentiable proxies for the model's features of interest. The resulting gradients enable the estimation of a local active subspace of the model within a defined sampling region. This approach enables efficient dimensionality reductions of highly non-linear search spaces at a low computational cost. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics, and improves the goodness-of-fit of the model, all within contained run-time costs.

In this study, we present a simple multi-channel framework for contrastive learning (MC-SimCLR) to encode 'what' and 'where' of spatial audios. MC-SimCLR learns joint spectral and spatial representations from unlabeled spatial audios, thereby enhancing both event classification and sound localization in downstream tasks. At its core, we propose a multi-level data augmentation pipeline that augments different levels of audio features, including waveforms, Mel spectrograms, and generalized cross-correlation (GCC) features. In addition, we introduce simple yet effective channel-wise augmentation methods to randomly swap the order of the microphones and mask Mel and GCC channels. By using these augmentations, we find that linear layers on top of the learned representation significantly outperform supervised models in terms of both event classification accuracy and localization error. We also perform a comprehensive analysis of the effect of each augmentation method and a comparison of the fine-tuning performance using different amounts of labeled data.

Computer vision research has long aimed to build systems that are robust to spatial transformations found in natural data. Traditionally, this is done using data augmentation or hard-coding invariances into the architecture. However, too much or too little invariance can hurt, and the correct amount is unknown a priori and dependent on the instance. Ideally, the appropriate invariance would be learned from data and inferred at test-time. We treat invariance as a prediction problem. Given any image, we use a normalizing flow to predict a distribution over transformations and average the predictions over them. Since this distribution only depends on the instance, we can align instances before classifying them and generalize invariance across classes. The same distribution can also be used to adapt to out-of-distribution poses. This normalizing flow is trained end-to-end and can learn a much larger range of transformations than Augerino and InstaAug. When used as data augmentation, our method shows accuracy and robustness gains on CIFAR 10, CIFAR10-LT, and TinyImageNet.

Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks.

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which performs well in long-horizon tasks. However, they are overly optimistic in stochastic environments with incorrect assumptions that the same goal can be consistently achieved by identical actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer (UNREST) for planning in stochastic driving environments without introducing additional transition or complex generative models. Specifically, UNREST estimates state uncertainties by the conditional mutual information between transitions and returns, and segments sequences accordingly. Discovering the `uncertainty accumulation' and `temporal locality' properties of driving environments, UNREST replaces the global returns in decision transformers with less uncertain truncated returns, to learn from true outcomes of agent actions rather than environment transitions. We also dynamically evaluate environmental uncertainty during inference for cautious planning. Extensive experimental results demonstrate UNREST's superior performance in various driving scenarios and the power of our uncertainty estimation strategy.

The recent criticisms of the robustness of post hoc model approximation explanation methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations. For each data point, abductive explanations provide a minimal subset of features that are sufficient to generate the outcome. While theoretically sound and rigorous, abductive explanations suffer from a major issue -- there can be several valid abductive explanations for the same data point. In such cases, providing a single abductive explanation can be insufficient; on the other hand, providing all valid abductive explanations can be incomprehensible due to their size. In this work, we solve this issue by aggregating the many possible abductive explanations into feature importance scores. We propose three aggregation methods: two based on power indices from cooperative game theory and a third based on a well-known measure of causal strength. We characterize these three methods axiomatically, showing that each of them uniquely satisfies a set of desirable properties. We also evaluate them on multiple datasets and show that these explanations are robust to the attacks that fool SHAP and LIME.

Consider an unknown nonlinear dynamical system that is known to be dissipative. The objective of this paper is to learn a neural dynamical model that approximates this system, while preserving the dissipativity property in the model. In general, imposing dissipativity constraints during neural network training is a hard problem for which no known techniques exist. In this work, we address the problem of learning a dissipative neural dynamical system model in two stages. First, we learn an unconstrained neural dynamical model that closely approximates the system dynamics. Next, we derive sufficient conditions to perturb the weights of the neural dynamical model to ensure dissipativity, followed by perturbation of the biases to retain the fit of the model to the trajectories of the nonlinear system. We show that these two perturbation problems can be solved independently to obtain a neural dynamical model that is guaranteed to be dissipative while closely approximating the nonlinear system.

Large pre-trained language models have demonstrated their proficiency in storing factual knowledge within their parameters and achieving remarkable results when fine-tuned for downstream natural language processing tasks. Nonetheless, their capacity to access and manipulate knowledge with precision remains constrained, resulting in performance disparities on knowledge-intensive tasks when compared to task-specific architectures. Additionally, the challenges of providing provenance for model decisions and maintaining up-to-date world knowledge persist as open research frontiers. To address these limitations, the integration of pre-trained models with differentiable access mechanisms to explicit non-parametric memory emerges as a promising solution. This survey delves into the realm of language models (LMs) augmented with the ability to tap into external knowledge sources, including external knowledge bases and search engines. While adhering to the standard objective of predicting missing tokens, these augmented LMs leverage diverse, possibly non-parametric external modules to augment their contextual processing capabilities, departing from the conventional language modeling paradigm. Through an exploration of current advancements in augmenting large language models with knowledge, this work concludes that this emerging research direction holds the potential to address prevalent issues in traditional LMs, such as hallucinations, un-grounded responses, and scalability challenges.

To leverage training data for the sufficient training of ML models from multiple parties in a confidentiality-preserving way, various collaborative distributed machine learning (CDML) system designs have been developed, for example, to perform assisted learning, federated learning, and split learning. CDML system designs show different traits, for example, high agent autonomy, machine learning (ML) model confidentiality, and fault tolerance. Facing a wide variety of CDML system designs with different traits, it is difficult for developers to design CDML systems with traits that match use case requirements in a targeted way. However, inappropriate CDML system designs may result in CDML systems failing their envisioned purposes. We developed a CDML design toolbox that can guide the development of CDML systems. Based on the CDML design toolbox, we present CDML system archetypes with distinct key traits that can support the design of CDML systems to meet use case requirements.

Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure. Active learning addresses this challenge by striving for optimum performance with minimal labeling cost by selecting the most informative and representative images for labeling. Despite its potential, active learning has been less explored in instance segmentation compared to other tasks like image classification, which require less labeling. In this study, we propose a post-hoc active learning algorithm that integrates uncertainty-based sampling with diversity-based sampling. Our proposed algorithm is not only simple and easy to implement, but it also delivers superior performance on various datasets. Its practical application is demonstrated on a real-world overhead imagery dataset, where it increases the labeling efficiency fivefold.

Currently, reinforcement learning (RL), especially deep RL, has received more and more attention in the research area. However, the security of RL has been an obvious problem due to the attack manners becoming mature. In order to defend against such adversarial attacks, several practical approaches are developed, such as adversarial training, data filtering, etc. However, these methods are mostly based on empirical algorithms and experiments, without rigorous theoretical analysis of the robustness of the algorithms. In this paper, we develop an algorithm to certify the robustness of a given policy offline with random smoothing, which could be proven and conducted as efficiently as ones without random smoothing. Experiments on different environments confirm the correctness of our algorithm.

We study infinite limits of neural network quantum states ($\infty$-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics. Ensemble averages of Renyi entropies are expressed in terms of neural network correlators, and architectures that exhibit volume-law entanglement are presented. A general framework is developed for studying the gradient descent dynamics of neural network quantum states (NNQS), using a quantum state neural tangent kernel (QS-NTK). For $\infty$-NNQS the training dynamics is simplified, since the QS-NTK becomes deterministic and constant. An analytic solution is derived for quantum state supervised learning, which allows an $\infty$-NNQS to recover any target wavefunction. Numerical experiments on finite and infinite NNQS in the transverse field Ising model and Fermi Hubbard model demonstrate excellent agreement with theory. $\infty$-NNQS opens up new opportunities for studying entanglement and training dynamics in other physics applications, such as in finding ground states.

Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research as they provide a structured way to integrate diverse information sources, enhancing AI system interpretability. This interpretability is crucial in healthcare, where trust and transparency matter, and eXplainable AI (XAI) supports decision making for healthcare professionals. This overview summarizes recent literature on the impact of KGs in healthcare and their role in developing explainable AI models. We cover KG workflow, including construction, relationship extraction, reasoning, and their applications in areas like Drug-Drug Interactions (DDI), Drug Target Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and bioinformatics. We emphasize the importance of making KGs more interpretable through knowledge-infused learning in healthcare. Finally, we highlight research challenges and provide insights for future directions.

The cost of hyperparameter tuning in deep learning has been rising with model sizes, prompting practitioners to find new tuning methods using a proxy of smaller networks. One such proposal uses $\mu$P parameterized networks, where the optimal hyperparameters for small width networks transfer to networks with arbitrarily large width. However, in this scheme, hyperparameters do not transfer across depths. As a remedy, we study residual networks with a residual branch scale of $1/\sqrt{\text{depth}}$ in combination with the $\mu$P parameterization. We provide experiments demonstrating that residual architectures including convolutional ResNets and Vision Transformers trained with this parameterization exhibit transfer of optimal hyperparameters across width and depth on CIFAR-10 and ImageNet. Furthermore, our empirical findings are supported and motivated by theory. Using recent developments in the dynamical mean field theory (DMFT) description of neural network learning dynamics, we show that this parameterization of ResNets admits a well-defined feature learning joint infinite-width and infinite-depth limit and show convergence of finite-size network dynamics towards this limit.

Recent advancements in Cryo-EM and protein structure prediction algorithms have made large-scale protein structures accessible, paving the way for machine learning-based functional annotations.The field of geometric deep learning focuses on creating methods working on geometric data. An essential aspect of learning from protein structures is representing these structures as a geometric object (be it a grid, graph, or surface) and applying a learning method tailored to this representation. The performance of a given approach will then depend on both the representation and its corresponding learning method. In this paper, we investigate representing proteins as $\textit{3D mesh surfaces}$ and incorporate them into an established representation benchmark. Our first finding is that despite promising preliminary results, the surface representation alone does not seem competitive with 3D grids. Building on this, we introduce a synergistic approach, combining surface representations with graph-based methods, resulting in a general framework that incorporates both representations in learning. We show that using this combination, we are able to obtain state-of-the-art results across $\textit{all tested tasks}$. Our code and data can be found online: https://github.com/Vincentx15/atom2D .

This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel formulation is devised, which does not necessitate the onerous computation of terminal ingredients, but rather relies on the explicit definition of a minimum prediction horizon ensuring closed-loop stability. The designed methodology is particularly suited for the control of systems learned by Recurrent Neural Networks (RNNs), which are known for their enhanced modeling capabilities and for which the incremental ISS properties can be studied thanks to simple algebraic conditions. The approach is applied to Gated Recurrent Unit (GRU) networks, providing also a method for the design of a tailored state observer with convergence guarantees. The resulting control architecture is tested on a benchmark system, demonstrating its good control performances and efficient applicability.

Eosinophilic Esophagitis (EoE) is an allergic condition increasing in prevalence. To diagnose EoE, pathologists must find 15 or more eosinophils within a single high-power field (400X magnification). Determining whether or not a patient has EoE can be an arduous process and any medical imaging approaches used to assist diagnosis must consider both efficiency and precision. We propose an improvement of Adorno et al's approach for quantifying eosinphils using deep image segmentation. Our new approach leverages Monte Carlo Dropout, a common approach in deep learning to reduce overfitting, to provide uncertainty quantification on current deep learning models. The uncertainty can be visualized in an output image to evaluate model performance, provide insight to how deep learning algorithms function, and assist pathologists in identifying eosinophils.

Progress in hybrid physics-machine learning (ML) climate simulations has been limited by the difficulty of obtaining performant coupled (i.e. online) simulations. While evaluating hundreds of ML parameterizations of subgrid closures (here of convection and radiation) offline is straightforward, online evaluation at the same scale is technically challenging. Our software automation achieves an order-of-magnitude larger sampling of online modeling errors than has previously been examined. Using this, we evaluate the hybrid climate model performance and define strategies to improve it. We show that model online performance improves when incorporating memory, a relative humidity input feature transformation, and additional input variables. We also reveal substantial variation in online error and inconsistencies between offline vs. online error statistics. The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices. This is considerably more sampling than tends to be reported in the current literature.

Searching for available parking spots in high-density urban centers is a stressful task for drivers that can be mitigated by systems that know in advance the nearest parking space available. To this end, image-based systems offer cost advantages over other sensor-based alternatives (e.g., ultrasonic sensors), requiring less physical infrastructure for installation and maintenance. Despite recent deep learning advances, deploying intelligent parking monitoring is still a challenge since most approaches involve collecting and labeling large amounts of data, which is laborious and time-consuming. Our study aims to uncover the challenges in creating a global framework, trained using publicly available labeled parking lot images, that performs accurately across diverse scenarios, enabling the parking space monitoring as a ready-to-use system to deploy in a new environment. Through exhaustive experiments involving different datasets and deep learning architectures, including fusion strategies and ensemble methods, we found that models trained on diverse datasets can achieve 95\% accuracy without the burden of data annotation and model training on the target parking lot

This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. In a contextual multi-armed bandit model, a novel bias (self-fulfilling bias) arises because the endogeneity of the data influences the choices of decisions, affecting the distribution of future data to be collected and analyzed. We propose an instrumental-variable-based algorithm to correct for the bias. It obtains true parameter values and attains low (logarithmic-like) regret levels. We also prove a central limit theorem for statistical inference. To establish the theoretical properties, we develop a general technique that untangles the interdependence between data and actions.

High-resolution aerial imagery allows fine details in the segmentation of farmlands. However, small objects and features introduce distortions to the delineation of object boundaries, and larger contextual views are needed to mitigate class confusion. In this work, we present an end-to-end trainable network for segmenting farmlands with contour levees from high-resolution aerial imagery. A fusion block is devised that includes multiple voting blocks to achieve image segmentation and classification. We integrate the fusion block with a backbone and produce both semantic predictions and segmentation slices. The segmentation slices are used to perform majority voting on the predictions. The network is trained to assign the most likely class label of a segment to its pixels, learning the concept of farmlands rather than analyzing constitutive pixels separately. We evaluate our method using images from the National Agriculture Imagery Program. Our method achieved an average accuracy of 94.34\%. Compared to the state-of-the-art methods, the proposed method obtains an improvement of 6.96% and 2.63% in the F1 score on average.

Prediction with the possibility of abstention (or selective prediction) is an important problem for error-critical machine learning applications. While well-studied in the classification setup, selective approaches to regression are much less developed. In this work, we consider the nonparametric heteroskedastic regression problem and develop an abstention procedure via testing the hypothesis on the value of the conditional variance at a given point. Unlike existing methods, the proposed one allows to account not only for the value of the variance itself but also for the uncertainty of the corresponding variance predictor. We prove non-asymptotic bounds on the risk of the resulting estimator and show the existence of several different convergence regimes. Theoretical analysis is illustrated with a series of experiments on simulated and real-world data.

In recent years, advances in the large-scale pretraining of language and text-to-image models have revolutionized the field of machine learning. Yet, integrating these two modalities into a single, robust model capable of generating seamless multimodal outputs remains a significant challenge. To address this gap, we present the Joint Autoregressive Mixture (JAM) framework, a modular approach that systematically fuses existing text and image generation models. We also introduce a specialized, data-efficient instruction-tuning strategy, tailored for mixed-modal generation tasks. Our final instruct-tuned model demonstrates unparalleled performance in generating high-quality multimodal outputs and represents the first model explicitly designed for this purpose.

Enabling robots to effectively imitate expert skills in longhorizon tasks such as locomotion, manipulation, and more, poses a long-standing challenge. Existing imitation learning (IL) approaches for robots still grapple with sub-optimal performance in complex tasks. In this paper, we consider how this challenge can be addressed within the human cognitive priors. Heuristically, we extend the usual notion of action to a dual Cognition (high-level)-Action (low-level) architecture by introducing intuitive human cognitive priors, and propose a novel skill IL framework through human-robot interaction, called Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent to effectively cognize and imitate the critical skills from raw visual demonstrations. CasIL enables both cognition and action imitation, while high-level skill cognition explicitly guides low-level primitive actions, providing robustness and reliability to the entire skill IL process. We evaluated our method on MuJoCo and RLBench benchmarks, as well as on the obstacle avoidance and point-goal navigation tasks for quadrupedal robot locomotion. Experimental results show that our CasIL consistently achieves competitive and robust skill imitation capability compared to other counterparts in a variety of long-horizon robotic tasks.

Daily fantasy sports (DFS) are weekly or daily online contests where real-game performances of individual players are converted to fantasy points (FPTS). Users select players for their lineup to maximize their FPTS within a set player salary cap. This paper focuses on (1) the development of a method to forecast NFL player performance under uncertainty and (2) determining an optimal lineup to maximize FPTS under a set salary limit. A supervised learning neural network was created and used to project FPTS based on past player performance (2018 NFL regular season for this work) prior to the upcoming week. These projected FPTS were used in a mixed integer linear program to find the optimal lineup. The performance of resultant lineups was compared to randomly-created lineups. On average, the optimal lineups outperformed the random lineups. The generated lineups were then compared to real-world lineups from users on DraftKings. The generated lineups generally fell in approximately the 31st percentile (median). The FPTS methods and predictions presented here can be further improved using this study as a baseline comparison.

Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space. Although the majority of these methods focus on static knowledge graphs, a large number of publicly available KGs contain temporal information stating the time instant/period over which a certain fact has been true. Such graphs are often known as temporal knowledge graphs. Furthermore, knowledge graphs may also contain textual descriptions of entities and relations. Both temporal information and textual descriptions are not taken into account during representation learning by static KGC methods, and only structural information of the graph is leveraged. Recently, some studies have used temporal information to improve link prediction, yet they do not exploit textual descriptions and do not support inductive inference (prediction on entities that have not been seen in training). We propose a novel framework called TEMT that exploits the power of pre-trained language models (PLMs) for text-enhanced temporal knowledge graph completion. The knowledge stored in the parameters of a PLM allows TEMT to produce rich semantic representations of facts and to generalize on previously unseen entities. TEMT leverages textual and temporal information available in a KG, treats them separately, and fuses them to get plausibility scores of facts. Unlike previous approaches, TEMT effectively captures dependencies across different time points and enables predictions on unseen entities. To assess the performance of TEMT, we carried out several experiments including time interval prediction, both in transductive and inductive settings, and triple classification. The experimental results show that TEMT is competitive with the state-of-the-art.

Bayesian optimization (BO), while proved highly effective for many black-box function optimization tasks, requires practitioners to carefully select priors that well model their functions of interest. Rather than specifying by hand, researchers have investigated transfer learning based methods to automatically learn the priors, e.g. multi-task BO (Swersky et al., 2013), few-shot BO (Wistuba and Grabocka, 2021) and HyperBO (Wang et al., 2022). However, those prior learning methods typically assume that the input domains are the same for all tasks, weakening their ability to use observations on functions with different domains or generalize the learned priors to BO on different search spaces. In this work, we present HyperBO+: a pre-training approach for hierarchical Gaussian processes that enables the same prior to work universally for Bayesian optimization on functions with different domains. We propose a two-step pre-training method and analyze its appealing asymptotic properties and benefits to BO both theoretically and empirically. On real-world hyperparameter tuning tasks that involve multiple search spaces, we demonstrate that HyperBO+ is able to generalize to unseen search spaces and achieves lower regrets than competitive baselines.

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP). Although some efforts based on syntactic analysis have opened the door to research in Quantum NLP (QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum self-attention neural network (QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability of unsupervised representation learning has become a limiting factor with regard to recent transparent-AI regulations. In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks.

Unsupervised fact verification aims to verify a claim using evidence from a trustworthy knowledge base without any kind of data annotation. To address this challenge, algorithms must produce features for every claim that are both semantically meaningful, and compact enough to find a semantic alignment with the source information. In contrast to previous work, which tackled the alignment problem by learning over annotated corpora of claims and their corresponding labels, we propose SFAVEL (Self-supervised Fact Verification via Language Model Distillation), a novel unsupervised framework that leverages pre-trained language models to distil self-supervised features into high-quality claim-fact alignments without the need for annotations. This is enabled by a novel contrastive loss function that encourages features to attain high-quality claim and evidence alignments whilst preserving the semantic relationships across the corpora. Notably, we present results that achieve a new state-of-the-art on the standard FEVER fact verification benchmark (+8% accuracy) with linear evaluation.

In, Elmarakeby et al., "Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET) was presented to model the state of prostate cancer. We verified the reproducibility of the study conducted by Elmarakeby et al., using both their original codebase, and our own re-implementation using more up-to-date libraries. We quantified the contribution of network sparsification by Reactome biological pathways, and confirmed its importance to P-NET's superior performance. Furthermore, we explored alternative neural architectures and approaches to incorporating biological information into the networks. We experimented with three types of graph neural networks on the same training data, and investigated the clinical prediction agreement between different models. Our analyses demonstrated that deep neural networks with distinct architectures make incorrect predictions for individual patient that are persistent across different initializations of a specific neural architecture. This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.

The onset of the COVID-19 pandemic accentuated the need for access to biomedical literature to answer timely and disease-specific questions. During the early days of the pandemic, one of the biggest challenges we faced was the lack of peer-reviewed biomedical articles on COVID-19 that could be used to train machine learning models for question answering (QA). In this paper, we explore the roles weak supervision and data augmentation play in training deep neural network QA models. First, we investigate whether labels generated automatically from the structured abstracts of scholarly papers using an information retrieval algorithm, BM25, provide a weak supervision signal to train an extractive QA model. We also curate new QA pairs using information retrieval techniques, guided by the clinicaltrials.gov schema and the structured abstracts of articles, in the absence of annotated data from biomedical domain experts. Furthermore, we explore augmenting the training data of a deep neural network model with linguistic features from external sources such as lexical databases to account for variations in word morphology and meaning. To better utilize our training data, we apply curriculum learning to domain adaptation, fine-tuning our QA model in stages based on characteristics of the QA pairs. We evaluate our methods in the context of QA models at the core of a system to answer questions about COVID-19.

Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. Results: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being greater than 0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have more than 85% chance of remaining AF free up until after seven years. Conclusion: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision-making and patient management processes.

Decoding memory content from brain activity during sleep has long been a goal in neuroscience. While spontaneous reactivation of memories during sleep in rodents is known to support memory consolidation and offline learning, capturing memory replay in humans is challenging due to the absence of well-annotated sleep datasets and the substantial differences in neural patterns between wakefulness and sleep. To address these challenges, we designed a novel cognitive neuroscience experiment and collected a comprehensive, well-annotated electroencephalography (EEG) dataset from 52 subjects during both wakefulness and sleep. Leveraging this benchmark dataset, we developed the Universal Sleep Decoder (USD) to align neural representations between wakefulness and sleep across subjects. Our model achieves up to 16.6% top-1 zero-shot accuracy on unseen subjects, comparable to decoding performances using individual sleep data. Furthermore, fine-tuning USD on test subjects enhances decoding accuracy to 25.9% top-1 accuracy, a substantial improvement over the baseline chance of 6.7%. Model comparison and ablation analyses reveal that our design choices, including the use of (i) an additional contrastive objective to integrate awake and sleep neural signals and (ii) the pretrain-finetune paradigm to incorporate different subjects, significantly contribute to these performances. Collectively, our findings and methodologies represent a significant advancement in the field of sleep decoding.

Robots with increasing autonomy progress our space exploration capabilities, particularly for in-situ exploration and sampling to stand in for human explorers. Currently, humans drive robots to meet scientific objectives, but depending on the robot's location, the exchange of information and driving commands between the human operator and robot may cause undue delays in mission fulfillment. An autonomous robot encoded with a scientific objective and an exploration strategy incurs no communication delays and can fulfill missions more quickly. Active learning algorithms offer this capability of intelligent exploration, but the underlying model structure varies the performance of the active learning algorithm in accurately forming an understanding of the environment. In this paper, we investigate the performance differences between active learning algorithms driven by Gaussian processes or Bayesian neural networks for exploration strategies encoded on agents that are constrained in their trajectories, like planetary surface rovers. These two active learning strategies were tested in a simulation environment against science-blind strategies to predict the spatial distribution of a variable of interest along multiple datasets. The performance metrics of interest are model accuracy in root mean squared (RMS) error, training time, model convergence, total distance traveled until convergence, and total samples until convergence. Active learning strategies encoded with Gaussian processes require less computation to train, converge to an accurate model more quickly, and propose trajectories of shorter distance, except in a few complex environments in which Bayesian neural networks achieve a more accurate model in the large data regime due to their more expressive functional bases. The paper concludes with advice on when and how to implement either exploration strategy for future space missions.

Additive manufacturing has revolutionized the manufacturing of complex parts by enabling direct material joining and offers several advantages such as cost-effective manufacturing of complex parts, reducing manufacturing waste, and opening new possibilities for manufacturing automation. One group of materials for which additive manufacturing holds great potential for enhancing component performance and properties is Functionally Graded Materials (FGMs). FGMs are advanced composite materials that exhibit smoothly varying properties making them desirable for applications in aerospace, automobile, biomedical, and defense industries. Such composition differs from traditional composite materials, since the location-dependent composition changes gradually in FGMs, leading to enhanced properties. Recently, machine learning techniques have emerged as a promising means for fabrication of FGMs through optimizing processing parameters, improving product quality, and detecting manufacturing defects. This paper first provides a brief literature review of works related to FGM fabrication, followed by reviewing works on employing machine learning in additive manufacturing, Afterward, we provide an overview of published works in the literature related to the application of machine learning methods in Directed Energy Deposition and for fabrication of FGMs.

Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy. However, when different clients' datasets are heterogeneous, traditional FL mechanisms produce a global model that does not adequately represent the poorer clients with limited data resources, resulting in lower accuracy and higher bias on their local data. According to the Matthew effect, which describes how the advantaged gain more advantage and the disadvantaged lose more over time, deploying such a global model in client applications may worsen the resource disparity among the clients and harm the principles of social welfare and fairness. To mitigate the Matthew effect, we propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian fairness refers to the global model learned from FL has: (1) equal accuracy among clients; (2) equal decision bias among clients. Besides achieving egalitarian fairness among the clients, EFFL also aims for performance optimality, minimizing the empirical risk loss and the bias for each client; both are essential for any ML model training, whether centralized or decentralized. We formulate EFFL as a constrained multi-constrained multi-objectives optimization (MCMOO) problem, with the decision bias and egalitarian fairness as constraints and the minimization of the empirical risk losses on all clients as multiple objectives to be optimized. We propose a gradient-based three-stage algorithm to obtain the Pareto optimal solutions within the constraint space. Extensive experiments demonstrate that EFFL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model with enhanced egalitarian fairness among all clients.

We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator and associated linear controller within an iterative loop. By prioritizing the task cost as main objective for controller learning, we reduce the reliance of controller design on a well-identified model, which extends Koopman control beyond low-dimensional systems to high-dimensional, complex nonlinear systems, including pixel-based scenarios.

Despite their success, Machine Learning (ML) models do not generalize effectively to data not originating from the training distribution. To reliably employ ML models in real-world healthcare systems and avoid inaccurate predictions on out-of-distribution (OOD) data, it is crucial to detect OOD samples. Numerous OOD detection approaches have been suggested in other fields - especially in computer vision - but it remains unclear whether the challenge is resolved when dealing with medical tabular data. To answer this pressing need, we propose an extensive reproducible benchmark to compare different methods across a suite of tests including both near and far OODs. Our benchmark leverages the latest versions of eICU and MIMIC-IV, two public datasets encompassing tens of thousands of ICU patients in several hospitals. We consider a wide array of density-based methods and SOTA post-hoc detectors across diverse predictive architectures, including MLP, ResNet, and Transformer. Our findings show that i) the problem appears to be solved for far-OODs, but remains open for near-OODs; ii) post-hoc methods alone perform poorly, but improve substantially when coupled with distance-based mechanisms; iii) the transformer architecture is far less overconfident compared to MLP and ResNet.

High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composing iterative generative processes, such as GFlowNets and diffusion models, is that to realize the desired target distribution, all steps of the generative process need to be coordinated, and satisfy delicate balance conditions. In this work, we propose Compositional Sculpting: a general approach for defining compositions of iterative generative processes. We then introduce a method for sampling from these compositions built on classifier guidance. We showcase ways to accomplish compositional sculpting in both GFlowNets and diffusion models. We highlight two binary operations $\unicode{x2014}$ the harmonic mean ($p_1 \otimes p_2$) and the contrast ($p_1 \unicode{x25D1}\,p_2$) between pairs, and the generalization of these operations to multiple component distributions. We offer empirical results on image and molecular generation tasks.  ( 2 min )

Copulas are a powerful statistical tool that captures dependencies across data dimensions. When applying Copulas, we can estimate multivariate distribution functions by initially estimating independent marginals, an easy task, and then a single copulating function, $C$, to connect the marginals, a hard task. For two-dimensional data, a copula is a two-increasing function of the form $C: (u,v)\in \mathbf{I}^2 \rightarrow \mathbf{I}$, where $\mathbf{I} = [0, 1]$. In this paper, we show how Neural Networks (NNs) can approximate any two-dimensional copula non-parametrically. Our approach, denoted as 2-Cats, is inspired by the Physics-Informed Neural Networks and Sobolev Training literature. Not only do we show that we can estimate the output of a 2d Copula better than the state-of-the-art, our approach is non-parametric and respects the mathematical properties of a Copula $C$.

Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizations, which are the preferred approach to problems with free surfaces or complex physics, remain largely unexplored. We present LagrangeBench, the first benchmarking suite for Lagrangian particle problems, focusing on temporal coarse-graining. In particular, our contribution is: (a) seven new fluid mechanics datasets (four in 2D and three in 3D) generated with the Smoothed Particle Hydrodynamics (SPH) method including the Taylor-Green vortex, lid-driven cavity, reverse Poiseuille flow, and dam break, each of which includes different physics like solid wall interactions or free surface, (b) efficient JAX-based API with various recent training strategies and neighbors search routine, and (c) JAX implementation of established Graph Neural Networks (GNNs) like GNS and SEGNN with baseline results. Finally, to measure the performance of learned surrogates we go beyond established position errors and introduce physical metrics like kinetic energy MSE and Sinkhorn distance for the particle distribution. Our codebase is available under the URL: https://github.com/tumaer/lagrangebench

Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function. To ensure the quality of the model, transfer learning approaches have been developed to automatically design GP priors by learning from observations on "training" functions. These training functions are typically required to have the same domain as the "test" function (black-box function to be optimized). In this paper, we introduce MPHD, a model pre-training method on heterogeneous domains, which uses a neural net mapping from domain-specific contexts to specifications of hierarchical GPs. MPHD can be seamlessly integrated with BO to transfer knowledge across heterogeneous search spaces. Our theoretical and empirical results demonstrate the validity of MPHD and its superior performance on challenging black-box function optimization tasks.

The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment. Reinforcement Learning from Human Feedback (RLHF) has emerged as a promising pathway towards AI alignment but brings forth challenges due to its complexity and dependence on a separate reward model. Direct Preference Optimization (DPO) has been proposed as an alternative, and it remains equivalent to RLHF under the reverse KL regularization constraint. This paper presents $f$-DPO, a generalized approach to DPO by incorporating diverse divergence constraints. We show that under certain $f$-divergences, including Jensen-Shannon divergence, forward KL divergences and $\alpha$-divergences, the complex relationship between the reward and optimal policy can also be simplified by addressing the Karush-Kuhn-Tucker conditions. This eliminates the need for estimating the normalizing constant in the Bradley-Terry model and enables a tractable mapping between the reward function and the optimal policy. Our approach optimizes LLMs to align with human preferences in a more efficient and supervised manner under a broad set of divergence constraints. Empirically, adopting these divergences ensures a balance between alignment performance and generation diversity. Importantly, $f$-DPO outperforms PPO-based methods in divergence efficiency, and divergence constraints directly influence expected calibration error (ECE).

Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: auditing and evaluation methods for private models, improvements of privacy-utility trade-offs, protection against a broad range of threats and attacks, differentially private generative models, and emerging application domains.

Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investigate a novel approach to generating realistic and useful exemplars and prototypes for time series data. Our approach uses a new form of time series average, the ShapeDTW Barycentric Average. We therefore turn our attention to accurately generating time series prototypes with a novel approach. The existing time series prototyping approaches rely on the Dynamic Time Warping (DTW) similarity measure such as DTW Barycentering Average (DBA) and SoftDBA. These last approaches suffer from a common problem of generating out-of-distribution artifacts in their prototypes. This is mostly caused by the DTW variant used and its incapability of detecting neighborhood similarities, instead it detects absolute similarities. Our proposed method, ShapeDBA, uses the ShapeDTW variant of DTW, that overcomes this issue. We chose time series clustering, a popular form of time series analysis to evaluate the outcome of ShapeDBA compared to the other prototyping approaches. Coupled with the k-means clustering algorithm, and evaluated on a total of 123 datasets from the UCR archive, our proposed averaging approach is able to achieve new state-of-the-art results in terms of Adjusted Rand Index.

Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability, and vulnerability to adversarial attacks. To address these challenges, Bayesian neural networks (BNNs) have emerged as a compelling extension of conventional neural networks, integrating uncertainty estimation into their predictive capabilities. This comprehensive primer presents a systematic introduction to the fundamental concepts of neural networks and Bayesian inference, elucidating their synergistic integration for the development of BNNs. The target audience comprises statisticians with a potential background in Bayesian methods but lacking deep learning expertise, as well as machine learners proficient in deep neural networks but with limited exposure to Bayesian statistics. We provide an overview of commonly employed priors, examining their impact on model behavior and performance. Additionally, we delve into the practical considerations associated with training and inference in BNNs. Furthermore, we explore advanced topics within the realm of BNN research, acknowledging the existence of ongoing debates and controversies. By offering insights into cutting-edge developments, this primer not only equips researchers and practitioners with a solid foundation in BNNs, but also illuminates the potential applications of this dynamic field. As a valuable resource, it fosters an understanding of BNNs and their promising prospects, facilitating further advancements in the pursuit of knowledge and innovation.

The standard paired-sample testing approach in the multidimensional setting applies multiple univariate tests on the individual features, followed by p-value adjustments. Such an approach suffers when the data carry numerous features. A number of studies have shown that classification accuracy can be seen as a proxy for two-sample testing. However, neither theoretical foundations nor practical recipes have been proposed so far on how this strategy could be extended to multidimensional paired-sample testing. In this work, we put forward the idea that scoring functions can be produced by the decision rules defined by the perpendicular bisecting hyperplanes of the line segments connecting each pair of instances. Then, the optimal scoring function can be obtained by the pseudomedian of those rules, which we estimate by extending naturally the Hodges-Lehmann estimator. We accordingly propose a framework of a two-step testing procedure. First, we estimate the bisecting hyperplanes for each pair of instances and an aggregated rule derived through the Hodges-Lehmann estimator. The paired samples are scored by this aggregated rule to produce a unidimensional representation. Second, we perform a Wilcoxon signed-rank test on the obtained representation. Our experiments indicate that our approach has substantial performance gains in testing accuracy compared to the traditional multivariate and multiple testing, while at the same time estimates each feature's contribution to the final result.

Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. See more results on our project page: https://realfill.github.io

Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions. However, it is not clear yet how to evaluate the correctness of those explanations, whether it is from a human or a model perspective. One unaddressed bottleneck in the current evaluation procedure is the problem of out-of-distribution explanations, whose distribution differs from those of the training data. This important issue affects existing evaluation metrics such as the popular faithfulness or fidelity score. In this paper, we show the limitations of faithfulness metrics. We propose GInX-Eval (Graph In-distribution eXplanation Evaluation), an evaluation procedure of graph explanations that overcomes the pitfalls of faithfulness and offers new insights on explainability methods. Using a retraining strategy, the GInX score measures how informative removed edges are for the model and the EdgeRank score evaluates if explanatory edges are correctly ordered by their importance. GInX-Eval verifies if ground-truth explanations are instructive to the GNN model. In addition, it shows that many popular methods, including gradient-based methods, produce explanations that are not better than a random designation of edges as important subgraphs, challenging the findings of current works in the area. Results with GInX-Eval are consistent across multiple datasets and align with human evaluation.

The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq. Clinical data, including demographics, comorbidities, lab results, and imaging, were collected and used to construct predictive models. These models, leveraging various machine learning algorithms, demonstrated commendable performance in identifying patients at risk. Early detection through these models promises timely interventions and improved outcomes. In conclusion, this research underscores the potential of data-driven machine learning for predicting post-COVID-19 cardiovascular complications, emphasizing the need for continued validation and research in diverse clinical settings.

In representation learning, regression has traditionally received less attention than classification. Directly applying representation learning techniques designed for classification to regression often results in fragmented representations in the latent space, yielding sub-optimal performance. In this paper, we argue that the potential of contrastive learning for regression has been overshadowed due to the neglect of two crucial aspects: ordinality-awareness and hardness. To address these challenges, we advocate "mixup your own contrastive pairs for supervised contrastive regression", instead of relying solely on real/augmented samples. Specifically, we propose Supervised Contrastive Learning for Regression with Mixup (SupReMix). It takes anchor-inclusive mixtures (mixup of the anchor and a distinct negative sample) as hard negative pairs and anchor-exclusive mixtures (mixup of two distinct negative samples) as hard positive pairs at the embedding level. This strategy formulates harder contrastive pairs by integrating richer ordinal information. Through extensive experiments on six regression datasets including 2D images, volumetric images, text, tabular data, and time-series signals, coupled with theoretical analysis, we demonstrate that SupReMix pre-training fosters continuous ordered representations of regression data, resulting in significant improvement in regression performance. Furthermore, SupReMix is superior to other approaches in a range of regression challenges including transfer learning, imbalanced training data, and scenarios with fewer training samples.

Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for desirable generations. A notable subject that is understudied is the (in-)consistency of RMs -- whether they can recognize the semantic changes to different prompts and appropriately adapt their reward assignments -- and their impact on the downstream RLHF model. In this paper, we visit a series of research questions relevant to RM inconsistency: (1) How can we measure the consistency of reward models? (2) How consistent are the existing RMs and how can we improve them? (3) In what ways does reward inconsistency influence the chatbots resulting from the RLHF model training? We propose Contrast Instructions -- a benchmarking strategy for the consistency of RM. Each example in Contrast Instructions features a pair of lexically similar instructions with different ground truth responses. A consistent RM is expected to rank the corresponding instruction and response higher than other combinations. We observe that current RMs trained with the standard ranking objective fail miserably on Contrast Instructions compared to average humans. To show that RM consistency can be improved efficiently without using extra training budget, we propose two techniques ConvexDA and RewardFusion, which enhance reward consistency through extrapolation during the RM training and inference stage, respectively. We show that RLHF models trained with a more consistent RM yield more useful responses, suggesting that reward inconsistency exhibits a trickle-down effect on the downstream RLHF process.

Anomaly detection is critical to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of attacks and CPS themselves, anomaly detection in CPS is becoming more and more challenging. In our previous work, we proposed a digital twin-based anomaly detection method, called ATTAIN, which takes advantage of both historical and real-time data of CPS. However, such data vary significantly in terms of difficulty. Therefore, similar to human learning processes, deep learning models (e.g., ATTAIN) can benefit from an easy-to-difficult curriculum. To this end, in this paper, we present a novel approach, named digitaL twin-based Anomaly deTecTion wIth Curriculum lEarning (LATTICE), which extends ATTAIN by introducing curriculum learning to optimize its learning paradigm. LATTICE attributes each sample with a difficulty score, before being fed into a training scheduler. The training scheduler samples batches of training data based on these difficulty scores such that learning from easy to difficult data can be performed. To evaluate LATTICE, we use five publicly available datasets collected from five real-world CPS testbeds. We compare LATTICE with ATTAIN and two other state-of-the-art anomaly detectors. Evaluation results show that LATTICE outperforms the three baselines and ATTAIN by 0.906%-2.367% in terms of the F1 score. LATTICE also, on average, reduces the training time of ATTAIN by 4.2% on the five datasets and is on par with the baselines in terms of detection delay time.

In this study, we leveraged machine learning techniques to identify risk factors associated with post-COVID-19 mental health disorders. Our analysis, based on data collected from 669 patients across various provinces in Iraq, yielded valuable insights. We found that age, gender, and geographical region of residence were significant demographic factors influencing the likelihood of developing mental health disorders in post-COVID-19 patients. Additionally, comorbidities and the severity of COVID-19 illness were important clinical predictors. Psychosocial factors, such as social support, coping strategies, and perceived stress levels, also played a substantial role. Our findings emphasize the complex interplay of multiple factors in the development of mental health disorders following COVID-19 recovery. Healthcare providers and policymakers should consider these risk factors when designing targeted interventions and support systems for individuals at risk. Machine learning-based approaches can provide a valuable tool for predicting and preventing adverse mental health outcomes in post-COVID-19 patients. Further research and prospective studies are needed to validate these findings and enhance our understanding of the long-term psychological impact of the COVID-19 pandemic. This study contributes to the growing body of knowledge regarding the mental health consequences of the COVID-19 pandemic and underscores the importance of a multidisciplinary approach to address the diverse needs of individuals on the path to recovery. Keywords: COVID-19, mental health, risk factors, machine learning, Iraq

Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s), and these proximity matrices are induced kernels. While there has been extensive research on the applications and properties of kernels, there is relatively little research on kernels induced by decision forests. We construct Kernel Mean Embedding Random Forests (KMERF), which induce kernels from random trees and/or forests using leaf-node proximity. We introduce the notion of an asymptotically characteristic kernel, and prove that KMERF kernels are asymptotically characteristic for both discrete and continuous data. Because KMERF is data-adaptive, we suspected it would outperform kernels selected a priori on finite sample data. We illustrate that KMERF nearly dominates current state-of-the-art kernel-based tests across a diverse range of high-dimensional two-sample and independence testing settings. Furthermore, our forest-based approach is interpretable, and provides feature importance metrics that readily distinguish important dimensions, unlike other high-dimensional non-parametric testing procedures. Hence, this work demonstrates the decision forest-based kernel can be more powerful and more interpretable than existing methods, flying in the face of conventional wisdom of the trade-off between the two.

Modern DNN-based recommendation systems rely on training-derived embeddings of sparse features. Input sparsity makes obtaining high-quality embeddings for rarely-occurring categories harder as their representations are updated infrequently. We demonstrate a training-time technique to produce superior embeddings via effective cross-category learning and theoretically explain its surprising effectiveness. The scheme, termed the multi-layer embeddings training (MLET), trains embeddings using factorization of the embedding layer, with an inner dimension higher than the target embedding dimension. For inference efficiency, MLET converts the trained two-layer embedding into a single-layer one thus keeping inference-time model size unchanged. Empirical superiority of MLET is puzzling as its search space is not larger than that of the single-layer embedding. The strong dependence of MLET on the inner dimension is even more surprising. We develop a theory that explains both of these behaviors by showing that MLET creates an adaptive update mechanism modulated by the singular vectors of embeddings. When tested on multiple state-of-the-art recommendation models for click-through rate (CTR) prediction tasks, MLET consistently produces better models, especially for rare items. At constant model quality, MLET allows embedding dimension, and model size, reduction by up to 16x, and 5.8x on average, across the models.

Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences. In this work, we propose the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework. By leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the exploratory process in reinforcement learning to address intricate long-horizon with sparse rewards robotic manipulation tasks. We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons. Results show IGE-LLMs (i) exhibit notably higher performance over related intrinsic methods and the direct use of LLMs in decision-making, (ii) can be combined and complement existing learning methods highlighting its modularity, (iii) are fairly insensitive to different intrinsic scaling parameters, and (iv) maintain robustness against increased levels of uncertainty and horizons.

Models with fewer parameters are necessary for the neural control of memory-limited, performant robots. Finding these smaller neural network architectures can be time-consuming. We propose HyperPPO, an on-policy reinforcement learning algorithm that utilizes graph hypernetworks to estimate the weights of multiple neural architectures simultaneously. Our method estimates weights for networks that are much smaller than those in common-use networks yet encode highly performant policies. We obtain multiple trained policies at the same time while maintaining sample efficiency and provide the user the choice of picking a network architecture that satisfies their computational constraints. We show that our method scales well - more training resources produce faster convergence to higher-performing architectures. We demonstrate that the neural policies estimated by HyperPPO are capable of decentralized control of a Crazyflie2.1 quadrotor. Website: https://sites.google.com/usc.edu/hyperppo

Purpose: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. Participants: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. Methods: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. Results: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labelled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches.

This work proposes to measure the scope of a patent claim as the reciprocal of the self-information contained in this claim. A probability of occurrence of the claim is obtained from a language model and this probability is used to compute the self-information. Grounded in information theory, this approach is based on the assumption that an unlikely concept is more informative than a usual concept, insofar as it is more surprising. In turn, the more surprising the information required to defined the claim, the narrower its scope. Five language models are considered, ranging from simplest models (each word or character is assigned an identical probability) to intermediate models (using average word or character frequencies), to a large language model (GPT2). Interestingly, the scope resulting from the simplest language models is proportional to the reciprocal of the number of words or characters involved in the claim, a metric already used in previous works. Application is made to multiple series of patent claims directed to distinct inventions, where each series consists of claims devised to have a gradually decreasing scope. The performance of the language models is assessed with respect to several ad hoc tests. The more sophisticated the model, the better the results. I.e., the GPT2 probability model outperforms models based on word and character frequencies, which themselves outdo the simplest models based on word or character counts. Still, the character count appears to be a more reliable indicator than the word count.

With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising execution platform due to their fine-grained parallelism, low-power consumption, reconfigurability, and concurrent execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between the non-trivial FPGA development efforts and rapid emergence of new GNN models. In this paper, we propose GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment, and FPGA implementations of 6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with distinct topologies and scales. The results show that GNNHLS achieves up to 50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy reduction.

Abdominal multi-organ segmentation in computed tomography (CT) is crucial for many clinical applications including disease detection and treatment planning. Deep learning methods have shown unprecedented performance in this perspective. However, it is still quite challenging to accurately segment different organs utilizing a single network due to the vague boundaries of organs, the complex background, and the substantially different organ size scales. In this work we used make transformer-based model for training. It was found through previous years' competitions that basically all of the top 5 methods used CNN-based methods, which is likely due to the lack of data volume that prevents transformer-based methods from taking full advantage. The thousands of samples in this competition may enable the transformer-based model to have more excellent results. The results on the public validation set also show that the transformer-based model can achieve an acceptable result and inference time.

We propose a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with 65B parameters in 3-bit or 4-bit precision on as little as one 48GB GPU. Our method, modular low-rank adaptation (ModuLoRA), integrates any user-specified weight quantizer with finetuning via low-rank adapters (LoRAs). Our approach relies on a simple quantization-agnostic backward pass that adaptively materializes low-precision LLM weights from a custom black-box quantization module. This approach enables finetuning 3-bit LLMs for the first time--leveraging state-of-the-art 3-bit OPTQ quantization often outperforms finetuning that relies on less sophisticated 4-bit and 8-bit methods. In our experiments, ModuLoRA attains competitive performance on text classification, natural language infernece, and instruction following tasks using significantly less memory than existing approaches, and we also surpass the state-of-the-art ROUGE score on a popular summarization task. We release ModuLoRA together with a series of low-precision models--including the first family of 3-bit instruction following Alpaca LLMs--as part of LLMTOOLS, a user-friendly library for quantizing, running, and finetuning LLMs on consumer GPUs.

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.

We provide results that exactly quantify how data augmentation affects the variance and limiting distribution of estimates, and analyze several specific models in detail. The results confirm some observations made in machine learning practice, but also lead to unexpected findings: Data augmentation may increase rather than decrease the uncertainty of estimates, such as the empirical prediction risk. It can act as a regularizer, but fails to do so in certain high-dimensional problems, and it may shift the double-descent peak of an empirical risk. Overall, the analysis shows that several properties data augmentation has been attributed with are not either true or false, but rather depend on a combination of factors -- notably the data distribution, the properties of the estimator, and the interplay of sample size, number of augmentations, and dimension. Our main theoretical tool is a limit theorem for functions of randomly transformed, high-dimensional random vectors. The proof draws on work in probability on noise stability of functions of many variables.

Advancements in nanotechnology and material science are paving the way toward nanoscale devices that combine sensing, computing, data and energy storage, and wireless communication. In precision medicine, these nanodevices show promise for disease diagnostics, treatment, and monitoring from within the patients' bloodstreams. Assigning the location of a sensed biological event with the event itself, which is the main proposition of flow-guided in-body nanoscale localization, would be immensely beneficial from the perspective of precision medicine. The nanoscale nature of the nanodevices and the challenging environment that the bloodstream represents, result in current flow-guided localization approaches being constrained in their communication and energy-related capabilities. The communication and energy constraints of the nanodevices result in different features of raw data for flow-guided localization, in turn affecting its performance. An analytical modeling of the effects of imperfect communication and constrained energy causing intermittent operation of the nanodevices on the raw data produced by the nanodevices would be beneficial. Hence, we propose an analytical model of raw data for flow-guided localization, where the raw data is modeled as a function of communication and energy-related capabilities of the nanodevice. We evaluate the model by comparing its output with the one obtained through the utilization of a simulator for objective evaluation of flow-guided localization, featuring comparably higher level of realism. Our results across a number of scenarios and heterogeneous performance metrics indicate high similarity between the model and simulator-generated raw datasets.

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a complete and luxuriant ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia.

Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions. In this paper, we develop a new spatial extremes model that has flexible and non-stationary dependence properties, and we integrate it in the encoding-decoding structure of a variational autoencoder (XVAE), whose parameters are estimated via variational Bayes combined with deep learning. The XVAE can be used as a spatio-temporal emulator that characterizes the distribution of potential mechanistic model output states and produces outputs that have the same statistical properties as the inputs, especially in the tail. As an aside, our approach also provides a novel way of making fast inference with complex extreme-value processes. Through extensive simulation studies, we show that our XVAE is substantially more time-efficient than traditional Bayesian inference while also outperforming many spatial extremes models with a stationary dependence structure. To further demonstrate the computational power of the XVAE, we analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes 30 years of daily measurements at 16703 grid cells. We find that the extremal dependence strength is weaker in the interior of Red Sea and it has decreased slightly over time.

The well-known governing physics in science and engineering is often based on certain assumptions and approximations. Therefore, analyses and designs carried out based on these equations are also approximate. The emergence of data-driven models has, to a certain degree, addressed this challenge; however, the purely data-driven models often (a) lack interpretability, (b) are data-hungry, and (c) do not generalize beyond the training window. Operator learning has recently been proposed as a potential alternative to address the aforementioned challenges; however, the challenges are still persistent. We here argue that one of the possible solutions resides in data-physics fusion, where the data-driven model is used to correct/identify the missing physics. To that end, we propose a novel Differentiable Physics Augmented Wavelet Neural Operator (DPA-WNO). The proposed DPA-WNO blends a differentiable physics solver with the Wavelet Neural Operator (WNO), where the role of WNO is to model the missing physics. This empowers the proposed framework to exploit the capability of WNO to learn from data while retaining the interpretability and generalizability associated with physics-based solvers. We illustrate the applicability of the proposed approach in solving time-dependent uncertainty quantification problems due to randomness in the initial condition. Four benchmark uncertainty quantification and reliability analysis examples from various fields of science and engineering are solved using the proposed approach. The results presented illustrate interesting features of the proposed approach.

We study the excess minimum risk in statistical inference, defined as the difference between the minimum expected loss in estimating a random variable from an observed feature vector and the minimum expected loss in estimating the same random variable from a transformation (statistic) of the feature vector. After characterizing lossless transformations, i.e., transformations for which the excess risk is zero for all loss functions, we construct a partitioning test statistic for the hypothesis that a given transformation is lossless and show that for i.i.d. data the test is strongly consistent. More generally, we develop information-theoretic upper bounds on the excess risk that uniformly hold over fairly general classes of loss functions. Based on these bounds, we introduce the notion of a delta-lossless transformation and give sufficient conditions for a given transformation to be universally delta-lossless. Applications to classification, nonparametric regression, portfolio strategies, information bottleneck, and deep learning, are also surveyed.

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.

In many areas of decision-making, forecasting is an essential pillar. Consequently, many different forecasting methods have been proposed. From our experience, recently presented forecasting methods are computationally intensive, poorly automated, tailored to a particular data set, or they lack a predictable time-to-result. To this end, we introduce Telescope, a novel machine learning-based forecasting approach that automatically retrieves relevant information from a given time series and splits it into parts, handling each of them separately. In contrast to deep learning methods, our approach doesn't require parameterization or the need to train and fit a multitude of parameters. It operates with just one time series and provides forecasts within seconds without any additional setup. Our experiments show that Telescope outperforms recent methods by providing accurate and reliable forecasts while making no assumptions about the analyzed time series.

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.

Fair machine learning research has been primarily concerned with classification tasks that result in discrimination. However, as machine learning algorithms are applied in new contexts the harms and injustices that result are qualitatively different than those presently studied. The existing research paradigm in machine learning which develops metrics and definitions of fairness cannot account for these qualitatively different types of injustice. One example of this is the problem of algorithmic collusion and market fairness. The negative consequences of algorithmic collusion affect all consumers, not only particular members of a protected class. Drawing on this case study, I propose an ethical framework for researchers and practitioners in machine learning seeking to develop and apply fairness metrics that extends to new domains. This contribution ties the development of formal metrics of fairness to specifically scoped normative principles. This enables fairness metrics to reflect different concerns from discrimination. I conclude with the limitations of my proposal and discuss promising avenues for future research.

In root finding and optimization, there are many cases where there is a closed set $A$ one likes that the sequence constructed by one's favourite method will not converge to A (here, we do not assume extra properties on $A$ such as being convex or connected). For example, if one wants to find roots, and one chooses initial points in the basin of attraction for 1 root $x^*$ (a fact which one may not know before hand), then one will always end up in that root. In this case, one would like to have a mechanism to avoid this point $z^*$ in the next runs of one's algorithm. In this paper, we propose two new methods aiming to achieve this. In the first method, we divide the cost function by an appropriate power of the distance function to $A$. This idea is inspired by how one would try to find all roots of a function in 1 variable. In the second method, which is more suitable for constrained optimization, we redefine the value of the function to be a big constant on $A$. We also propose, based on this, an algorithm to escape the basin of attraction of a component of positive dimension to reach another component. As an application, we prove a rigorous guarantee for finding roots of a meromorphic function of 1 complex variable in a given domain. Along the way, we compare with main existing relevant methods in the current literature. We provide several examples in various different settings to illustrate the usefulness of the new approach.

The current learning process of deep learning, regardless of any deep neural network (DNN) architecture and/or learning algorithm used, is essentially a single resolution training. We explore multiresolution learning and show that multiresolution learning can significantly improve robustness of DNN models for both 1D signal and 2D signal (image) prediction problems. We demonstrate this improvement in terms of both noise and adversarial robustness as well as with small training dataset size. Our results also suggest that it may not be necessary to trade standard accuracy for robustness with multiresolution learning, which is, interestingly, contrary to the observation obtained from the traditional single resolution learning setting.

Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenges in reasoning about gripper-object interactions. In this work, we introduce Hybrid Actor-Critic Maps for Manipulation (HACMan), a reinforcement learning approach for 6D non-prehensile manipulation of objects using point cloud observations. HACMan proposes a temporally-abstracted and spatially-grounded object-centric action representation that consists of selecting a contact location from the object point cloud and a set of motion parameters describing how the robot will move after making contact. We modify an existing off-policy RL algorithm to learn in this hybrid discrete-continuous action representation. We evaluate HACMan on a 6D object pose alignment task in both simulation and in the real world. On the hardest version of our task, with randomized initial poses, randomized 6D goals, and diverse object categories, our policy demonstrates strong generalization to unseen object categories without a performance drop, achieving an 89% success rate on unseen objects in simulation and 50% success rate with zero-shot transfer in the real world. Compared to alternative action representations, HACMan achieves a success rate more than three times higher than the best baseline. With zero-shot sim2real transfer, our policy can successfully manipulate unseen objects in the real world for challenging non-planar goals, using dynamic and contact-rich non-prehensile skills. Videos can be found on the project website: https://hacman-2023.github.io.

Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to model various NP-hard combinatorial optimization problems in the form of binary variables. The Hamiltonian function is often used to formulate QUBO problems where it is used as the objective function in the context of optimization. Recently, PI-GNN, a generic scalable framework, has been proposed to address the Combinatorial Optimization (CO) problems over graphs based on a simple Graph Neural Network (GNN) architecture. Their novel contribution was a generic QUBO-formulated Hamiltonian-inspired loss function that was optimized using GNN. In this study, we address a crucial issue related to the aforementioned setup especially observed in denser graphs. The reinforcement learning-based paradigm has also been widely used to address numerous CO problems. Here we also formulate and empirically evaluate the compatibility of the QUBO-formulated Hamiltonian as the generic reward function in the Reinforcement Learning paradigm to directly integrate the actual node projection status during training as the form of rewards. In our experiments, we observed up to 44% improvement in the RL-based setup compared to the PI-GNN algorithm. Our implementation can be found in https://github.com/rizveeredwan/learning-graph-structure.

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.

In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the remaining dataset, the associated computational costs have driven the development of efficient, approximate unlearning techniques. Moving beyond data-centric MU approaches, our study introduces a novel model-based perspective: model sparsification via weight pruning, which is capable of reducing the gap between exact unlearning and approximate unlearning. We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. This leads to a new MU paradigm, termed prune first, then unlearn, which infuses a sparse model prior into the unlearning process. Building on this insight, we also develop a sparsity-aware unlearning method that utilizes sparsity regularization to enhance the training process of approximate unlearning. Extensive experiments show that our proposals consistently benefit MU in various unlearning scenarios. A notable highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest unlearning methods) when using sparsity-aware unlearning. Furthermore, we demonstrate the practical impact of our proposed MU methods in addressing other machine learning challenges, such as defending against backdoor attacks and enhancing transfer learning. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse.

We provide a simple convergence proof for AdaGrad optimizing non-convex objectives under only affine noise variance and bounded smoothness assumptions. The proof is essentially based on a novel auxiliary function $\xi$ that helps eliminate the complexity of handling the correlation between the numerator and denominator of AdaGrad's update. Leveraging simple proofs, we are able to obtain tighter results than existing results \citep{faw2022power} and extend the analysis to several new and important cases. Specifically, for the over-parameterized regime, we show that AdaGrad needs only $\mathcal{O}(\frac{1}{\varepsilon^2})$ iterations to ensure the gradient norm smaller than $\varepsilon$, which matches the rate of SGD and significantly tighter than existing rates $\mathcal{O}(\frac{1}{\varepsilon^4})$ for AdaGrad. We then discard the bounded smoothness assumption and consider a realistic assumption on smoothness called $(L_0,L_1)$-smooth condition, which allows local smoothness to grow with the gradient norm. Again based on the auxiliary function $\xi$, we prove that AdaGrad succeeds in converging under $(L_0,L_1)$-smooth condition as long as the learning rate is lower than a threshold. Interestingly, we further show that the requirement on learning rate under the $(L_0,L_1)$-smooth condition is necessary via proof by contradiction, in contrast with the case of uniform smoothness conditions where convergence is guaranteed regardless of learning rate choices. Together, our analyses broaden the understanding of AdaGrad and demonstrate the power of the new auxiliary function in the investigations of AdaGrad.

We introduce Transformer-VQ, a decoder-only transformer computing softmax-based dense self-attention in linear time. Transformer-VQ's efficient attention is enabled by vector-quantized keys and a novel caching mechanism. In large-scale experiments, Transformer-VQ is shown highly competitive in quality, with strong results on Enwik8 (0.99 bpb), PG-19 (26.6 ppl), and ImageNet64 (3.16 bpb). Code: https://github.com/transformer-vq/transformer_vq

We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data points may be extremely large. To date, the vast majority of work on DP SO assumes that the loss is uniformly Lipschitz continuous over data (i.e. stochastic gradients are uniformly bounded over all data points). While this assumption is convenient, it often leads to pessimistic excess risk bounds. In many practical problems, the worst-case (uniform) Lipschitz parameter of the loss over all data points may be extremely large due to outliers. In such cases, the error bounds for DP SO, which scale with the worst-case Lipschitz parameter of the loss, are vacuous. To address these limitations, this work provides near-optimal excess risk bounds that do not depend on the uniform Lipschitz parameter of the loss. Building on a recent line of work (Wang et al., 2020; Kamath et al., 2022), we assume that stochastic gradients have bounded $k$-th order moments for some $k \geq 2$. Compared with works on uniformly Lipschitz DP SO, our excess risk scales with the $k$-th moment bound instead of the uniform Lipschitz parameter of the loss, allowing for significantly faster rates in the presence of outliers and/or heavy-tailed data. For convex and strongly convex loss functions, we provide the first asymptotically optimal excess risk bounds (up to a logarithmic factor). In contrast to (Wang et al., 2020; Kamath et al., 2022), our bounds do not require the loss function to be differentiable/smooth. We also devise a linear-time algorithm for smooth losses that has excess risk that is tight in certain practical parameter regimes. Additionally, our work is the first to address non-convex non-uniformly Lipschitz loss functions satisfying the Proximal-PL inequality; this covers some practical machine learning models. Our Proximal-PL algorithm has near-optimal excess risk.

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.

When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical. However, most existing methods for detecting distribution shift are not well-suited to robotics settings, where data often arrives in a streaming fashion and may be very high-dimensional. In this work, we present an online method for detecting distribution shift with guarantees on the false positive rate - i.e., when there is no distribution shift, our system is very unlikely (with probability $< \epsilon$) to falsely issue an alert; any alerts that are issued should therefore be heeded. Our method is specifically designed for efficient detection even with high dimensional data, and it empirically achieves up to 11x faster detection on realistic robotics settings compared to prior work while maintaining a low false negative rate in practice (whenever there is a distribution shift in our experiments, our method indeed emits an alert). We demonstrate our approach in both simulation and hardware for a visual servoing task, and show that our method indeed issues an alert before a failure occurs.

Variance in predictions across different trained models is a significant, under-explored source of error in fair classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approach and make four overarching contributions: We 1) Define a metric called self-consistency, derived from variance, which we use as a proxy for measuring and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary; 3) Conduct the largest to-date empirical study of the role of variance (vis-a-vis self-consistency and arbitrariness) in fair classification; and, 4) Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily usable for future research. Altogether, our experiments reveal shocking insights about the reliability of conclusions on benchmark datasets. Most fairness classification benchmarks are close-to-fair when taking into account the amount of arbitrariness present in predictions -- before we even try to apply common fairness interventions. This finding calls into question the practical utility of common algorithmic fairness methods, and in turn suggests that we should fundamentally reconsider how we choose to measure fairness in machine learning.

The logical analysis of data, LAD, is a technique that yields two-class classifiers based on Boolean functions having disjunctive normal form (DNF) representation. Although LAD algorithms employ optimization techniques, the resulting binary classifiers or binary rules do not lead to overfitting. We propose a theoretical justification for the absence of overfitting by estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models where hypothesis sets consist of DNFs with a small number of cubic monomials. We illustrate and confirm our observations empirically.

Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure. For this reason, we build open source infrastructure to train neural exchange correlation functionals. We aim to standardize the processing pipeline by adapting state-of-the-art techniques from work done by multiple groups. We have open sourced the model in the DeepChem library to provide a platform for additional research on differentiable quantum chemistry methods.

The soft Dice loss (SDL) has taken a pivotal role in numerous automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superior functioning have been uncovered and further optimizations have been explored. However, there is currently no implementation that supports its direct utilization in scenarios involving soft labels. Hence, a synergy between the use of SDL and research leveraging the use of soft labels, also in the context of model calibration, is still missing. In this work, we introduce Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a standard setting with hard labels, but (ii) can be employed in settings with soft labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm the potential synergy of DMLs with soft labels (e.g.\ averaging, label smoothing, and knowledge distillation) over hard labels (e.g.\ majority voting and random selection). As a result, we obtain superior Dice scores and model calibration, which supports the wider adoption of DMLs in practice. The code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically-grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones -- local causal states -- capture organized behaviors and coherent structures in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering coherent structures in two real world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events -- hurricanes and atmospheric rivers -- and discover other novel coherent structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction

Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces have been proposed in the literature. These algorithms mainly use either hyperplanes or geodesics for decision boundaries in a large margin classifiers setting leading to a non-convex optimization problem. In this paper, we propose a novel large margin classifier based on horospherical decision boundaries that leads to a geodesically convex optimization problem that can be optimized using any Riemannian gradient descent technique guaranteeing a globally optimal solution. We present several experiments depicting the competitive performance of our classifier in comparison to SOTA.

The generation of adversarial inputs has become a crucial issue in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. However, the problem poses multiple practical challenges, including scalability issues owing to large-sized networks, and the generation of adversarial inputs that lack important qualities such as naturalness and output-impartiality. This problem shares its end goal with the task of patching neural nets where small changes in some of the network's weights need to be discovered so that upon applying these changes, the modified net produces the desirable output for a given set of inputs. We exploit this connection by proposing to obtain an adversarial input from a patch, with the underlying observation that the effect of changing the weights can also be brought about by changing the inputs instead. Thus, this paper presents a novel way to generate input perturbations that are adversarial for a given network by using an efficient network patching technique. We note that the proposed method is significantly more effective than the prior state-of-the-art techniques.

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the kinetic energy density functional, which is notoriously hard to approximate for non-periodic molecular systems. In this work, we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep-learning functional model. We build the essential nonlocality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges therein, M-OFDFT achieves a comparable accuracy with Kohn-Sham DFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those in training, which unleashes the appealing scaling for studying large molecules including proteins, representing an advancement of the accuracy-efficiency trade-off frontier in quantum chemistry.

Scene representation has been a crucial design choice in robotic manipulation systems. An ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse manipulation tasks. However, previous works often lack all three properties simultaneously. In this work, we introduce D$^3$Fields - dynamic 3D descriptor fields. These fields capture the dynamics of the underlying 3D environment and encode both semantic features and instance masks. Specifically, we project arbitrary 3D points in the workspace onto multi-view 2D visual observations and interpolate features derived from foundational models. The resulting fused descriptor fields allow for flexible goal specifications using 2D images with varied contexts, styles, and instances. To evaluate the effectiveness of these descriptor fields, we apply our representation to a wide range of robotic manipulation tasks in a zero-shot manner. Through extensive evaluation in both real-world scenarios and simulations, we demonstrate that D$^3$Fields are both generalizable and effective for zero-shot robotic manipulation tasks. In quantitative comparisons with state-of-the-art dense descriptors, such as Dense Object Nets and DINO, D$^3$Fields exhibit significantly better generalization abilities and manipulation accuracy.

Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies on globally clear boundaries between benign and infected model updates. However, model updates are easy to be mixed and scattered throughout in reality due to the diverse distributions of local data. This work focuses on excluding infected models in FL. Unlike previous perspectives from a global view, we propose Snowball, a novel anti-backdoor FL framework through bidirectional elections from an individual perspective inspired by one principle deduced by us and two principles in FL and deep learning. It is characterized by a) bottom-up election, where each candidate model update votes to several peer ones such that a few model updates are elected as selectees for aggregation; and b) top-down election, where selectees progressively enlarge themselves through picking up from the candidates. We compare Snowball with state-of-the-art defenses to backdoor attacks in FL on five real-world datasets, demonstrating its superior resistance to backdoor attacks and slight impact on the accuracy of the global model.

Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. Despite this, several operations are still performed in a sequential manner. For instance, the forward and backward passes are executed layer-by-layer, and the output of diffusion models is produced by applying a sequence of denoising steps. This sequential approach results in a computational cost proportional to the number of steps involved, presenting a potential bottleneck as the number of steps increases. In this work, we introduce DeepPCR, a novel algorithm which parallelizes typically sequential operations used in inference and training of neural networks. DeepPCR is based on interpreting a sequence of $L$ steps as the solution of a specific system of equations, which we recover using the Parallel Cyclic Reduction algorithm. This reduces the complexity of computing the sequential operations from $\mathcal{O}(L)$ to $\mathcal{O}(\log_2L)$, thus yielding a speedup for large $L$. To verify the theoretical lower complexity of the algorithm, and to identify regimes for speedup, we test the effectiveness of DeepPCR in parallelizing the forward and backward pass in multi-layer perceptrons, and reach speedups of up to $30\times$ for forward and $200\times$ for backward pass. We additionally showcase the flexibility of DeepPCR by parallelizing training of ResNets with as many as 1024 layers, and generation in diffusion models, enabling up to $7\times$ faster training and $11\times$ faster generation, respectively, when compared to the sequential approach.

We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated independently from the distribution of the arm chosen at each time. However, these rewards are not directly observed. Instead, with a fixed $\varepsilon\in (0,\frac{1}{2})$, the agent observes a sample from the chosen arm's distribution with probability $1-\varepsilon$, or from an arbitrary corruption distribution with probability $\varepsilon$. Importantly, we impose no assumptions on these corruption distributions, which can be unbounded. In this setting, accommodating potentially unbounded corruptions, we establish a problem-dependent lower bound on regret for a given family of arm distributions. We introduce CRIMED, an asymptotically-optimal algorithm that achieves the exact lower bound on regret for bandits with Gaussian distributions with known variance. Additionally, we provide a finite-sample analysis of CRIMED's regret performance. Notably, CRIMED can effectively handle corruptions with $\varepsilon$ values as high as $\frac{1}{2}$. Furthermore, we develop a tight concentration result for medians in the presence of arbitrary corruptions, even with $\varepsilon$ values up to $\frac{1}{2}$, which may be of independent interest. We also discuss an extension of the algorithm for handling misspecification in Gaussian model.

Continual Learning methods are designed to learn new tasks without erasing previous knowledge. However, Continual Learning often requires massive computational power and storage capacity for satisfactory performance. In this paper, we propose a resource-efficient continual learning method called the Elastic Expansion Network (E2Net). Leveraging core subnet distillation and precise replay sample selection, E2Net achieves superior average accuracy and diminished forgetting within the same computational and storage constraints, all while minimizing processing time. In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks. To enhance storage resource utilization, we then propose Subnet Constraint Experience Replay to optimize rehearsal efficiency through a sample storage strategy based on the structures of representative networks. Extensive experiments conducted predominantly on cloud environments with diverse datasets and also spanning the edge environment demonstrate that E2Net consistently outperforms state-of-the-art methods. In addition, our method outperforms competitors in terms of both storage and computational requirements.

Women are at higher risk of Alzheimer's and other neurological diseases after menopause, and yet research connecting female brain health to sex hormone fluctuations is limited. We seek to investigate this connection by developing tools that quantify 3D shape changes that occur in the brain during sex hormone fluctuations. Geodesic regression on the space of 3D discrete surfaces offers a principled way to characterize the evolution of a brain's shape. However, in its current form, this approach is too computationally expensive for practical use. In this paper, we propose approximation schemes that accelerate geodesic regression on shape spaces of 3D discrete surfaces. We also provide rules of thumb for when each approximation can be used. We test our approach on synthetic data to quantify the speed-accuracy trade-off of these approximations and show that practitioners can expect very significant speed-up while only sacrificing little accuracy. Finally, we apply the method to real brain shape data and produce the first characterization of how the female hippocampus changes shape during the menstrual cycle as a function of progesterone: a characterization made (practically) possible by our approximation schemes. Our work paves the way for comprehensive, practical shape analyses in the fields of bio-medicine and computer vision. Our implementation is publicly available on GitHub: https://github.com/bioshape-lab/my28brains.

5G introduced modularized network functions (NFs) to support emerging services in a more flexible and elastic manner. To mitigate the complexity in such modularized NF management, automated network operation and management are indispensable, and thus the 3rd generation partnership project (3GPP) has introduced a network data analytics function (NWDAF). However, a conventional NWDAF needs to conduct both inference and training tasks, and thus it is difficult to provide the analytics results to NFs in a timely manner for an increased number of analytics requests. In this article, we propose a hierarchical network data analytics framework (H-NDAF) where inference tasks are distributed to multiple leaf NWDAFs and training tasks are conducted at the root NWDAF. Extensive simulation results using open-source software (i.e., free5GC) demonstrate that H-NDAF can provide sufficiently accurate analytics and faster analytics provision time compared to the conventional NWDAF.

Class imbalance is a major problem in many real world classification tasks. Due to the imbalance in the number of samples, the support vector machine (SVM) classifier gets biased toward the majority class. Furthermore, these samples are often observed with a certain degree of noise. Therefore, to remove these problems we propose a novel fuzzy based approach to deal with class imbalanced as well noisy datasets. We propose two approaches to address these problems. The first approach is based on the intuitionistic fuzzy membership, termed as robust energy-based intuitionistic fuzzy least squares twin support vector machine (IF-RELSTSVM). Furthermore, we introduce the concept of hyperplane-based fuzzy membership in our second approach, where the final classifier is termed as robust energy-based fuzzy least square twin support vector machine (F-RELSTSVM). By using this technique, the membership values are based on a projection based approach, where the data points are projected on the hyperplanes. The performance of the proposed algorithms is evaluated on several benchmark and synthetic datasets. The experimental results show that the proposed IF-RELSTSVM and F-RELSTSVM models outperform the baseline algorithms. Statistical tests are performed to check the significance of the proposed algorithms. The results show the applicability of the proposed algorithms on noisy as well as imbalanced datasets.

Vision Transformer (ViT) has emerged as a powerful architecture in the realm of modern computer vision. However, its application in certain imaging fields, such as microscopy and satellite imaging, presents unique challenges. In these domains, images often contain multiple channels, each carrying semantically distinct and independent information. Furthermore, the model must demonstrate robustness to sparsity in input channels, as they may not be densely available during training or testing. In this paper, we propose a modification to the ViT architecture that enhances reasoning across the input channels and introduce Hierarchical Channel Sampling (HCS) as an additional regularization technique to ensure robustness when only partial channels are presented during test time. Our proposed model, ChannelViT, constructs patch tokens independently from each input channel and utilizes a learnable channel embedding that is added to the patch tokens, similar to positional embeddings. We evaluate the performance of ChannelViT on ImageNet, JUMP-CP (microscopy cell imaging), and So2Sat (satellite imaging). Our results show that ChannelViT outperforms ViT on classification tasks and generalizes well, even when a subset of input channels is used during testing. Across our experiments, HCS proves to be a powerful regularizer, independent of the architecture employed, suggesting itself as a straightforward technique for robust ViT training. Lastly, we find that ChannelViT generalizes effectively even when there is limited access to all channels during training, highlighting its potential for multi-channel imaging under real-world conditions with sparse sensors.

In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting. We incorporate a collection of datasets obtained from diverse, dynamic systems and real-life records. Each dataset is standardized by dividing it into training and test trajectories with predetermined lookback lengths. We include trajectories of length up to $2000$ to ensure a reliable evaluation of long-term forecasting capabilities. To determine the most effective model in diverse scenarios, we conduct an extensive benchmarking analysis using classical and state-of-the-art models, namely LSTM, DeepAR, NLinear, N-Hits, PatchTST, and LatentODE. Our findings reveal intriguing performance comparisons among these models, highlighting the dataset-dependent nature of model effectiveness. Notably, we introduce a custom latent NLinear model and enhance DeepAR with a curriculum learning phase. Both consistently outperform their vanilla counterparts.

Quantifying the dependence between high-dimensional random variables is central to statistical learning and inference. Two classical methods are canonical correlation analysis (CCA), which identifies maximally correlated projected versions of the original variables, and Shannon's mutual information, which is a universal dependence measure that also captures high-order dependencies. However, CCA only accounts for linear dependence, which may be insufficient for certain applications, while mutual information is often infeasible to compute/estimate in high dimensions. This work proposes a middle ground in the form of a scalable information-theoretic generalization of CCA, termed max-sliced mutual information (mSMI). mSMI equals the maximal mutual information between low-dimensional projections of the high-dimensional variables, which reduces back to CCA in the Gaussian case. It enjoys the best of both worlds: capturing intricate dependencies in the data while being amenable to fast computation and scalable estimation from samples. We show that mSMI retains favorable structural properties of Shannon's mutual information, like variational forms and identification of independence. We then study statistical estimation of mSMI, propose an efficiently computable neural estimator, and couple it with formal non-asymptotic error bounds. We present experiments that demonstrate the utility of mSMI for several tasks, encompassing independence testing, multi-view representation learning, algorithmic fairness, and generative modeling. We observe that mSMI consistently outperforms competing methods with little-to-no computational overhead.

Current methods of imitation learning (IL), primarily based on deep neural networks, offer efficient means for obtaining driving policies from real-world data but suffer from significant limitations in interpretability and generalizability. These shortcomings are particularly concerning in safety-critical applications like autonomous driving. In this paper, we address these limitations by introducing Symbolic Imitation Learning (SIL), a groundbreaking method that employs Inductive Logic Programming (ILP) to learn driving policies which are transparent, explainable and generalisable from available datasets. Utilizing the real-world highD dataset, we subject our method to a rigorous comparative analysis against prevailing neural-network-based IL methods. Our results demonstrate that SIL not only enhances the interpretability of driving policies but also significantly improves their applicability across varied driving situations. Hence, this work offers a novel pathway to more reliable and safer autonomous driving systems, underscoring the potential of integrating ILP into the domain of IL.

This work reports the empirical performance of an automated medical landmark detection method for predict clinical markers in hip radiograph images. Notably, the detection method was trained using a label-only augmentation scheme; our results indicate that this form of augmentation outperforms traditional data augmentation and produces highly sample efficient estimators. We train a generic U-Net-based architecture under a curriculum consisting of two phases: initially relaxing the landmarking task by enlarging the label points to regions, then gradually eroding these label regions back to the base task. We measure the benefits of this approach on six datasets of radiographs with gold-standard expert annotations.  ( 2 min )

Modern streaming data categorization faces significant challenges from concept drift and class imbalanced data. This negatively impacts the output of the classifier, leading to improper classification. Furthermore, other factors such as the overlapping of multiple classes limit the extent of the correctness of the output. This work proposes a novel framework for integrating data pre-processing and dynamic ensemble selection, by formulating the classification framework for the nonstationary drifting imbalanced data stream, which employs the data pre-processing and dynamic ensemble selection techniques. The proposed framework was evaluated using six artificially generated data streams with differing imbalance ratios in combination with two different types of concept drifts. Each stream is composed of 200 chunks of 500 objects described by eight features and contains five concept drifts. Seven pre-processing techniques and two dynamic ensemble selection methods were considered. According to experimental results, data pre-processing combined with Dynamic Ensemble Selection techniques significantly delivers more accuracy when dealing with imbalanced data streams.

High-quality face images are required to guarantee the stability and reliability of automatic face recognition (FR) systems in surveillance and security scenarios. However, a massive amount of face data is usually compressed before being analyzed due to limitations on transmission or storage. The compressed images may lose the powerful identity information, resulting in the performance degradation of the FR system. Herein, we make the first attempt to study just noticeable difference (JND) for the FR system, which can be defined as the maximum distortion that the FR system cannot notice. More specifically, we establish a JND dataset including 3530 original images and 137,670 compressed images generated by advanced reference encoding/decoding software based on the Versatile Video Coding (VVC) standard (VTM-15.0). Subsequently, we develop a novel JND prediction model to directly infer JND images for the FR system. In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i.e., identity and residual features, via self-supervised learning. Then, the residual feature is fed into the decoder to generate the residual map. Finally, the predicted JND map is obtained by subtracting the residual map from the original image. Experimental results have demonstrated that the proposed model achieves higher accuracy of JND map prediction compared with the state-of-the-art JND models, and is capable of saving more bits while maintaining the performance of the FR system compared with VTM-15.0.

We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver. Specifically, we are interested in the perception-distortion trade-off in the practical finite block length regime, in which separate source and channel coding can be highly suboptimal. We introduce a novel scheme that utilizes the range-null space decomposition of the target image. We transmit the range-space of the image after encoding and employ DDPM to progressively refine its null space contents. Through extensive experiments, we demonstrate significant improvements in distortion and perceptual quality of reconstructed images compared to standard DeepJSCC and the state-of-the-art generative learning-based method. We will publicly share our source code to facilitate further research and reproducibility.

Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts under the same training conditions in the few-parameter regime. However, the general performance of large-scale quantum models is difficult to examine because of the limited size of quantum circuits, which can be reliably implemented in the near future. We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent. In the classical-to-quantum transfer learning framework, a QCNN can solve complex classification problems without requiring a large-scale quantum circuit by utilizing a pre-trained classical convolutional neural network (CNN). We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data. The results show that transfer learning from classical to quantum CNN performs considerably better than purely classical transfer learning models under similar training conditions.

Hierarchical Variational Autoencoders (VAEs) are among the most popular likelihood-based generative models. There is a consensus that the top-down hierarchical VAEs allow effective learning of deep latent structures and avoid problems like posterior collapse. Here, we show that this is not necessarily the case, and the problem of collapsing posteriors remains. To discourage this issue, we propose a deep hierarchical VAE with a context on top. Specifically, we use a Discrete Cosine Transform to obtain the last latent variable. In a series of experiments, we observe that the proposed modification allows us to achieve better utilization of the latent space and does not harm the model's generative abilities.

As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone. However, several challenges may create a crisis of trust in current artificial intelligence systems that need to be bridged: 1) Insufficient explanation of predictive results; 2) Inadequate generalization for learning models; 3) Poor adaptability to uncertain environments. Consequently, we explore a neural program to bridge trustworthiness and open-world learning, extending from single-modal to multi-modal scenarios for readers. 1) To enhance design-level interpretability, we first customize trustworthy networks with specific physical meanings; 2) We then design environmental well-being task-interfaces via flexible learning regularizers for improving the generalization of trustworthy learning; 3) We propose to increase the robustness of trustworthy learning by integrating open-world recognition losses with agent mechanisms. Eventually, we enhance various trustworthy properties through the establishment of design-level explainability, environmental well-being task-interfaces and open-world recognition programs. These designed open-world protocols are applicable across a wide range of surroundings, under open-world multimedia recognition scenarios with significant performance improvements observed.

In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data. In this work, we address this limitation by providing an approach for inferring latent graphs that capture the intrinsic data relationships. By leveraging graph-based representations, our approach facilitates the seamless propagation of information throughout the graph, enabling the effective incorporation of global and local knowledge. Through evaluations on biomedical tabular datasets, we compare the capabilities of our approach to other contemporary methods. Our work demonstrates the significance of inter-instance relationship discovery as practical means for constructing robust latent graphs to enhance semi-supervised learning techniques. Our method achieves state-of-the-art results on three biomedical datasets.

Sharpness-aware minimization (SAM) has well documented merits in enhancing generalization of deep neural networks, even without sizable data augmentation. Embracing the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing the maximum loss caused by an adversary perturbing parameters within the neighborhood. Although critical to account for sharpness of the loss function, such an 'over-friendly adversary' can curtail the outmost level of generalization. The novel approach of this contribution fosters stabilization of adversaries through variance suppression (VaSSO) to avoid such friendliness. VaSSO's provable stability safeguards its numerical improvement over SAM in model-agnostic tasks, including image classification and machine translation. In addition, experiments confirm that VaSSO endows SAM with robustness against high levels of label noise.

The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.

Large Language Models (LLMs) have proven their exceptional capabilities in performing language-related tasks. However, their deployment poses significant challenges due to their considerable memory and storage requirements. In response to this issue, weight-only quantization, particularly 3 and 4-bit weight-only quantization, has emerged as one of the most viable solutions. As the number of bits decreases, the quantization grid broadens, thus emphasizing the importance of up and down rounding. While previous studies have demonstrated that fine-tuning up and down rounding with the addition of perturbations can enhance accuracy in some scenarios, our study is driven by the precise and limited boundary of these perturbations, where only the threshold for altering the rounding value is of significance. Consequently, we propose a concise and highly effective approach for optimizing the weight rounding task. Our method, named SignRound, involves lightweight block-wise tuning using signed gradient descent, enabling us to achieve outstanding results within 400 steps. SignRound competes impressively against recent methods without introducing additional inference overhead. The source code will be publicly available at \url{https://github.com/intel/neural-compressor} soon.

Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to allow them to operate in complex, high-dimensional, non-linear, and dynamically changing environments. Due to the complexity of these environments, DNN classifiers may output misclassifications during operation when they face domains not identified during development. Removing a system from operation for retraining becomes impractical as the number of such AS increases. To increase AS reliability and overcome this limitation, DNN classifiers need to have the ability to adapt during operation when faced with different operational domains using a few samples (e.g. 100 samples). However, retraining DNNs on a few samples is known to cause catastrophic forgetting. In this paper, we introduce Dynamic Incremental Regularised Adaptation (DIRA), a framework for operational domain adaption of DNN classifiers using regularisation techniques to overcome catastrophic forgetting and achieve adaptation when retraining using a few samples of the target domain. Our approach shows improvements on different image classification benchmarks aimed at evaluating robustness to distribution shifts (e.g.CIFAR-10C/100C, ImageNet-C), and produces state-of-the-art performance in comparison with other frameworks from the literature.

Many emerging user-facing services adopt Graph Neural Networks (GNNs) to improve serving accuracy. When the graph used by a GNN model changes, representations (embedding) of nodes in the graph should be updated accordingly. However, the node representation update is too slow, resulting in either long response latency of user queries (the inference is performed after the update completes) or high staleness problem (the inference is performed based on stale data). Our in-depth analysis shows that the slow update is mainly due to neighbor explosion problem in graphs and duplicated computation. Based on such findings, we propose STAG, a GNN serving framework that enables low latency and low staleness of GNN-based services. It comprises a collaborative serving mechanism and an additivity-based incremental propagation strategy. With the collaborative serving mechanism, only part of node representations are updated during the update phase, and the final representations are calculated in the inference phase. It alleviates the neighbor explosion problem. The additivity-based incremental propagation strategy reuses intermediate data during the update phase, eliminating duplicated computation problem. Experimental results show that STAG accelerates the update phase by 1.3x~90.1x, and greatly reduces staleness time with a slight increase in response latency.

Exact computation of the partition function is known to be intractable, necessitating approximate inference techniques. Existing methods for approximate inference are slow to converge for many benchmarks. The control of accuracy-complexity trade-off is also non-trivial in many of these methods. We propose a novel incremental build-infer-approximate (IBIA) framework for approximate inference that addresses these issues. In this framework, the probabilistic graphical model is converted into a sequence of clique tree forests (SCTF) with bounded clique sizes. We show that the SCTF can be used to efficiently compute the partition function. We propose two new algorithms which are used to construct the SCTF and prove the correctness of both. The first is an algorithm for incremental construction of CTFs that is guaranteed to give a valid CTF with bounded clique sizes and the second is an approximation algorithm that takes a calibrated CTF as input and yields a valid and calibrated CTF with reduced clique sizes as the output. We have evaluated our method using several benchmark sets from recent UAI competitions and our results show good accuracies with competitive runtimes.

Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s), and these proximity matrices are induced kernels. While there has been extensive research on the applications and properties of kernels, there is relatively little research on kernels induced by decision forests. We construct Kernel Mean Embedding Random Forests (KMERF), which induce kernels from random trees and/or forests using leaf-node proximity. We introduce the notion of an asymptotically characteristic kernel, and prove that KMERF kernels are asymptotically characteristic for both discrete and continuous data. Because KMERF is data-adaptive, we suspected it would outperform kernels selected a priori on finite sample data. We illustrate that KMERF nearly dominates current state-of-the-art kernel-based tests across a diverse range of high-dimensional two-sample and independence testing settings. Furthermore, our forest-based approach is interpretable, and provides feature importance metrics that readily distinguish important dimensions, unlike other high-dimensional non-parametric testing procedures. Hence, this work demonstrates the decision forest-based kernel can be more powerful and more interpretable than existing methods, flying in the face of conventional wisdom of the trade-off between the two.

Regularization plays a crucial role in machine learning models, especially for deep neural networks. The existing regularization techniques mainly rely on the i.i.d. assumption and only consider the knowledge from the current sample, without the leverage of the neighboring relationship between samples. In this work, we propose a general regularizer called \textbf{Patch-level Neighborhood Interpolation~(Pani)} that conducts a non-local representation in the computation of networks. Our proposal explicitly constructs patch-level graphs in different layers and then linearly interpolates neighborhood patch features, serving as a general and effective regularization strategy. Further, we customize our approach into two kinds of popular regularization methods, namely Virtual Adversarial Training (VAT) and MixUp as well as its variants. The first derived \textbf{Pani VAT} presents a novel way to construct non-local adversarial smoothness by employing patch-level interpolated perturbations. The second derived \textbf{Pani MixUp} method extends the MixUp, and achieves superiority over MixUp and competitive performance over state-of-the-art variants of MixUp method with a significant advantage in computational efficiency. Extensive experiments have verified the effectiveness of our Pani approach in both supervised and semi-supervised settings.

We study the multiclass classification problem where the features come from the mixture of time-homogeneous diffusions. Specifically, the classes are discriminated by their drift functions while the diffusion coefficient is common to all classes and unknown. In this framework, we build a plug-in classifier which relies on nonparametric estimators of the drift and diffusion functions. We first establish the consistency of our classification procedure under mild assumptions and then provide rates of cnvergence under different set of assumptions. Finally, a numerical study supports our theoretical findings.

The growing penetration of intermittent, renewable generation in US power grids, especially wind and solar generation, results in increased operational uncertainty. In that context, accurate forecasts are critical, especially for wind generation, which exhibits large variability and is historically harder to predict. To overcome this challenge, this work proposes a novel Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques. The BPR framework first learns an intermediate hierarchy level (the bundles), then predicts wind power at the asset, bundle, and fleet level, and finally reconciles all forecasts to ensure consistency. This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks. The paper also introduces new asset-bundling criteria that capture the spatio-temporal dynamics of wind power time series. Extensive numerical experiments are conducted on an industry-size dataset of 283 wind farms in the MISO footprint. The experiments consider short-term and day-ahead forecasts, and evaluates a large variety of forecasting models that include weather predictions as covariates. The results demonstrate the benefits of BPR, which consistently and significantly improves forecast accuracy over baselines, especially at the fleet level.

We investigate the high-dimensional properties of robust regression estimators in the presence of heavy-tailed contamination of both the covariates and response functions. In particular, we provide a sharp asymptotic characterisation of M-estimators trained on a family of elliptical covariate and noise data distributions including cases where second and higher moments do not exist. We show that, despite being consistent, the Huber loss with optimally tuned location parameter $\delta$ is suboptimal in the high-dimensional regime in the presence of heavy-tailed noise, highlighting the necessity of further regularisation to achieve optimal performance. This result also uncovers the existence of a curious transition in $\delta$ as a function of the sample complexity and contamination. Moreover, we derive the decay rates for the excess risk of ridge regression. We show that, while it is both optimal and universal for noise distributions with finite second moment, its decay rate can be considerably faster when the covariates' second moment does not exist. Finally, we show that our formulas readily generalise to a richer family of models and data distributions, such as generalised linear estimation with arbitrary convex regularisation trained on mixture models.

Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions. In this paper, we develop a new spatial extremes model that has flexible and non-stationary dependence properties, and we integrate it in the encoding-decoding structure of a variational autoencoder (XVAE), whose parameters are estimated via variational Bayes combined with deep learning. The XVAE can be used as a spatio-temporal emulator that characterizes the distribution of potential mechanistic model output states and produces outputs that have the same statistical properties as the inputs, especially in the tail. As an aside, our approach also provides a novel way of making fast inference with complex extreme-value processes. Through extensive simulation studies, we show that our XVAE is substantially more time-efficient than traditional Bayesian inference while also outperforming many spatial extremes models with a stationary dependence structure. To further demonstrate the computational power of the XVAE, we analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes 30 years of daily measurements at 16703 grid cells. We find that the extremal dependence strength is weaker in the interior of Red Sea and it has decreased slightly over time.

Unsupervised fact verification aims to verify a claim using evidence from a trustworthy knowledge base without any kind of data annotation. To address this challenge, algorithms must produce features for every claim that are both semantically meaningful, and compact enough to find a semantic alignment with the source information. In contrast to previous work, which tackled the alignment problem by learning over annotated corpora of claims and their corresponding labels, we propose SFAVEL (Self-supervised Fact Verification via Language Model Distillation), a novel unsupervised framework that leverages pre-trained language models to distil self-supervised features into high-quality claim-fact alignments without the need for annotations. This is enabled by a novel contrastive loss function that encourages features to attain high-quality claim and evidence alignments whilst preserving the semantic relationships across the corpora. Notably, we present results that achieve a new state-of-the-art on the standard FEVER fact verification benchmark (+8% accuracy) with linear evaluation.  ( 2 min )

The cost of hyperparameter tuning in deep learning has been rising with model sizes, prompting practitioners to find new tuning methods using a proxy of smaller networks. One such proposal uses $\mu$P parameterized networks, where the optimal hyperparameters for small width networks transfer to networks with arbitrarily large width. However, in this scheme, hyperparameters do not transfer across depths. As a remedy, we study residual networks with a residual branch scale of $1/\sqrt{\text{depth}}$ in combination with the $\mu$P parameterization. We provide experiments demonstrating that residual architectures including convolutional ResNets and Vision Transformers trained with this parameterization exhibit transfer of optimal hyperparameters across width and depth on CIFAR-10 and ImageNet. Furthermore, our empirical findings are supported and motivated by theory. Using recent developments in the dynamical mean field theory (DMFT) description of neural network learning dynamics, we show that this parameterization of ResNets admits a well-defined feature learning joint infinite-width and infinite-depth limit and show convergence of finite-size network dynamics towards this limit.

Langevin dynamics are widely used in sampling high-dimensional, non-Gaussian distributions whose densities are known up to a normalizing constant. In particular, there is strong interest in unadjusted Langevin algorithms (ULA), which directly discretize Langevin dynamics to estimate expectations over the target distribution. We study the use of transport maps that approximately normalize a target distribution as a way to precondition and accelerate the convergence of Langevin dynamics. We show that in continuous time, when a transport map is applied to Langevin dynamics, the result is a Riemannian manifold Langevin dynamics (RMLD) with metric defined by the transport map. We also show that applying a transport map to an irreversibly-perturbed ULA results in a geometry-informed irreversible perturbation (GiIrr) of the original dynamics. These connections suggest more systematic ways of learning metrics and perturbations, and also yield alternative discretizations of the RMLD described by the map, which we study. Under appropriate conditions, these discretized processes can be endowed with non-asymptotic bounds describing convergence to the target distribution in 2-Wasserstein distance. Illustrative numerical results complement our theoretical claims.

We introduce the modified planar rotator method (MPRS), a physically inspired machine learning method for spatial/temporal regression. MPRS is a non-parametric model which incorporates spatial or temporal correlations via short-range, distance-dependent ``interactions'' without assuming a specific form for the underlying probability distribution. Predictions are obtained by means of a fully autonomous learning algorithm which employs equilibrium conditional Monte Carlo simulations. MPRS is able to handle scattered data and arbitrary spatial dimensions. We report tests on various synthetic and real-word data in one, two and three dimensions which demonstrate that the MPRS prediction performance (without parameter tuning) is competitive with standard interpolation methods such as ordinary kriging and inverse distance weighting. In particular, MPRS is a particularly effective gap-filling method for rough and non-Gaussian data (e.g., daily precipitation time series). MPRS shows superior computational efficiency and scalability for large samples. Massive data sets involving millions of nodes can be processed in a few seconds on a standard personal computer.  ( 2 min )

The standard paired-sample testing approach in the multidimensional setting applies multiple univariate tests on the individual features, followed by p-value adjustments. Such an approach suffers when the data carry numerous features. A number of studies have shown that classification accuracy can be seen as a proxy for two-sample testing. However, neither theoretical foundations nor practical recipes have been proposed so far on how this strategy could be extended to multidimensional paired-sample testing. In this work, we put forward the idea that scoring functions can be produced by the decision rules defined by the perpendicular bisecting hyperplanes of the line segments connecting each pair of instances. Then, the optimal scoring function can be obtained by the pseudomedian of those rules, which we estimate by extending naturally the Hodges-Lehmann estimator. We accordingly propose a framework of a two-step testing procedure. First, we estimate the bisecting hyperplanes for each pair of instances and an aggregated rule derived through the Hodges-Lehmann estimator. The paired samples are scored by this aggregated rule to produce a unidimensional representation. Second, we perform a Wilcoxon signed-rank test on the obtained representation. Our experiments indicate that our approach has substantial performance gains in testing accuracy compared to the traditional multivariate and multiple testing, while at the same time estimates each feature's contribution to the final result.  ( 2 min )

In this paper, we introduce a novel analysis of neural networks based on geometric (Clifford) algebra and convex optimization. We show that optimal weights of deep ReLU neural networks are given by the wedge product of training samples when trained with standard regularized loss. Furthermore, the training problem reduces to convex optimization over wedge product features, which encode the geometric structure of the training dataset. This structure is given in terms of signed volumes of triangles and parallelotopes generated by data vectors. The convex problem finds a small subset of samples via $\ell_1$ regularization to discover only relevant wedge product features. Our analysis provides a novel perspective on the inner workings of deep neural networks and sheds light on the role of the hidden layers.  ( 2 min )

Graphon estimation has been one of the most fundamental problems in network analysis and has received considerable attention in the past decade. From the statistical perspective, the minimax error rate of graphon estimation has been established by Gao et al (2015) for both stochastic block model (SBM) and nonparametric graphon estimation. The statistical optimal estimators are based on constrained least squares and have computational complexity exponential in the dimension. From the computational perspective, the best-known polynomial-time estimator is based on universal singular value thresholding (USVT), but it can only achieve a much slower estimation error rate than the minimax one. It is natural to wonder if such a gap is essential. The computational optimality of the USVT or the existence of a computational barrier in graphon estimation has been a long-standing open problem. In this work, we take the first step towards it and provide rigorous evidence for the computational barrier in graphon estimation via low-degree polynomials. Specifically, in both SBM and nonparametric graphon estimation, we show that for low-degree polynomial estimators, their estimation error rates cannot be significantly better than that of the USVT under a wide range of parameter regimes. Our results are proved based on the recent development of low-degree polynomials by Schramm and Wein (2022), while we overcome a few key challenges in applying it to the general graphon estimation problem. By leveraging our main results, we also provide a computational lower bound on the clustering error for community detection in SBM with a growing number of communities and this yields a new piece of evidence for the conjectured Kesten-Stigum threshold for efficient community recovery.

Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability, and vulnerability to adversarial attacks. To address these challenges, Bayesian neural networks (BNNs) have emerged as a compelling extension of conventional neural networks, integrating uncertainty estimation into their predictive capabilities. This comprehensive primer presents a systematic introduction to the fundamental concepts of neural networks and Bayesian inference, elucidating their synergistic integration for the development of BNNs. The target audience comprises statisticians with a potential background in Bayesian methods but lacking deep learning expertise, as well as machine learners proficient in deep neural networks but with limited exposure to Bayesian statistics. We provide an overview of commonly employed priors, examining their impact on model behavior and performance. Additionally, we delve into the practical considerations associated with training and inference in BNNs. Furthermore, we explore advanced topics within the realm of BNN research, acknowledging the existence of ongoing debates and controversies. By offering insights into cutting-edge developments, this primer not only equips researchers and practitioners with a solid foundation in BNNs, but also illuminates the potential applications of this dynamic field. As a valuable resource, it fosters an understanding of BNNs and their promising prospects, facilitating further advancements in the pursuit of knowledge and innovation.

Semi-supervised learning (SSL) is a promising approach for training deep classification models using labeled and unlabeled datasets. However, existing SSL methods rely on a large unlabeled dataset, which may not always be available in many real-world applications due to legal constraints (e.g., GDPR). In this paper, we investigate the research question: Can we train SSL models without real unlabeled datasets? Instead of using real unlabeled datasets, we propose an SSL method using synthetic datasets generated from generative foundation models trained on datasets containing millions of samples in diverse domains (e.g., ImageNet). Our main concepts are identifying synthetic samples that emulate unlabeled samples from generative foundation models and training classifiers using these synthetic samples. To achieve this, our method is formulated as an alternating optimization problem: (i) meta-learning of generative foundation models and (ii) SSL of classifiers using real labeled and synthetic unlabeled samples. For (i), we propose a meta-learning objective that optimizes latent variables to generate samples that resemble real labeled samples and minimize the validation loss. For (ii), we propose a simple unsupervised loss function that regularizes the feature extractors of classifiers to maximize the performance improvement obtained from synthetic samples. We confirm that our method outperforms baselines using generative foundation models on SSL. We also demonstrate that our methods outperform SSL using real unlabeled datasets in scenarios with extremely small amounts of labeled datasets. This suggests that synthetic samples have the potential to provide improvement gains more efficiently than real unlabeled data.  ( 3 min )

Batch reinforcement learning (RL) defines the task of learning from a fixed batch of data lacking exhaustive exploration. Worst-case optimality algorithms, which calibrate a value-function model class from logged experience and perform some type of pessimistic evaluation under the learned model, have emerged as a promising paradigm for batch RL. However, contemporary works on this stream have commonly overlooked the hierarchical decision-making structure hidden in the optimization landscape. In this paper, we adopt a game-theoretical viewpoint and model the policy learning diagram as a two-player general-sum game with a leader-follower structure. We propose a novel stochastic gradient-based learning algorithm: StackelbergLearner, in which the leader player updates according to the total derivative of its objective instead of the usual individual gradient, and the follower player makes individual updates and ensures transition-consistent pessimistic reasoning. The derived learning dynamic naturally lends StackelbergLearner to a game-theoretic interpretation and provides a convergence guarantee to differentiable Stackelberg equilibria. From a theoretical standpoint, we provide instance-dependent regret bounds with general function approximation, which shows that our algorithm can learn a best-effort policy that is able to compete against any comparator policy that is covered by batch data. Notably, our theoretical regret guarantees only require realizability without any data coverage and strong function approximation conditions, e.g., Bellman closedness, which is in contrast to prior works lacking such guarantees. Through comprehensive experiments, we find that our algorithm consistently performs as well or better as compared to state-of-the-art methods in batch RL benchmark and real-world datasets.

Contrastive learning is a self-supervised representation learning framework, where two positive views generated through data augmentation are made similar by an attraction force in a data representation space, while a repulsive force makes them far from negative examples. Non-contrastive learning, represented by BYOL and SimSiam, further gets rid of negative examples and improves computational efficiency. While learned representations may collapse into a single point due to the lack of the repulsive force at first sight, Tian et al. (2021) revealed through the learning dynamics analysis that the representations can avoid collapse if data augmentation is sufficiently stronger than regularization. However, their analysis does not take into account commonly-used feature normalization, a normalizer before measuring the similarity of representations, and hence excessively strong regularization may collapse the dynamics, which is an unnatural behavior under the presence of feature normalization. Therefore, we extend the previous theory based on the L2 loss by considering the cosine loss, which involves feature normalization. We show that the cosine loss induces sixth-order dynamics (while the L2 loss induces a third-order one), in which a stable equilibrium dynamically emerges even if there are only collapsed solutions with given initial parameters. Thus, we offer a new understanding that feature normalization plays an important role in robustly preventing the dynamics collapse.

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction  ( 2 min )

As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone. However, several challenges may create a crisis of trust in current artificial intelligence systems that need to be bridged: 1) Insufficient explanation of predictive results; 2) Inadequate generalization for learning models; 3) Poor adaptability to uncertain environments. Consequently, we explore a neural program to bridge trustworthiness and open-world learning, extending from single-modal to multi-modal scenarios for readers. 1) To enhance design-level interpretability, we first customize trustworthy networks with specific physical meanings; 2) We then design environmental well-being task-interfaces via flexible learning regularizers for improving the generalization of trustworthy learning; 3) We propose to increase the robustness of trustworthy learning by integrating open-world recognition losses with agent mechanisms. Eventually, we enhance various trustworthy properties through the establishment of design-level explainability, environmental well-being task-interfaces and open-world recognition programs. These designed open-world protocols are applicable across a wide range of surroundings, under open-world multimedia recognition scenarios with significant performance improvements observed.

Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces have been proposed in the literature. These algorithms mainly use either hyperplanes or geodesics for decision boundaries in a large margin classifiers setting leading to a non-convex optimization problem. In this paper, we propose a novel large margin classifier based on horospherical decision boundaries that leads to a geodesically convex optimization problem that can be optimized using any Riemannian gradient descent technique guaranteeing a globally optimal solution. We present several experiments depicting the competitive performance of our classifier in comparison to SOTA.

Pairwise comparison of graphs is key to many applications in Machine learning ranging from clustering, kernel-based classification/regression and more recently supervised graph prediction. Distances between graphs usually rely on informative representations of these structured objects such as bag of substructures or other graph embeddings. A recently popular solution consists in representing graphs as metric measure spaces, allowing to successfully leverage Optimal Transport, which provides meaningful distances allowing to compare them: the Gromov-Wasserstein distances. However, this family of distances overlooks edge attributes, which are essential for many structured objects. In this work, we introduce an extension of Gromov-Wasserstein distance for comparing graphs whose both nodes and edges have features. We propose novel algorithms for distance and barycenter computation. We empirically show the effectiveness of the novel distance in learning tasks where graphs occur in either input space or output space, such as classification and graph prediction.

We study the excess minimum risk in statistical inference, defined as the difference between the minimum expected loss in estimating a random variable from an observed feature vector and the minimum expected loss in estimating the same random variable from a transformation (statistic) of the feature vector. After characterizing lossless transformations, i.e., transformations for which the excess risk is zero for all loss functions, we construct a partitioning test statistic for the hypothesis that a given transformation is lossless and show that for i.i.d. data the test is strongly consistent. More generally, we develop information-theoretic upper bounds on the excess risk that uniformly hold over fairly general classes of loss functions. Based on these bounds, we introduce the notion of a delta-lossless transformation and give sufficient conditions for a given transformation to be universally delta-lossless. Applications to classification, nonparametric regression, portfolio strategies, information bottleneck, and deep learning, are also surveyed.

This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. In a contextual multi-armed bandit model, a novel bias (self-fulfilling bias) arises because the endogeneity of the data influences the choices of decisions, affecting the distribution of future data to be collected and analyzed. We propose an instrumental-variable-based algorithm to correct for the bias. It obtains true parameter values and attains low (logarithmic-like) regret levels. We also prove a central limit theorem for statistical inference. To establish the theoretical properties, we develop a general technique that untangles the interdependence between data and actions.

We provide results that exactly quantify how data augmentation affects the variance and limiting distribution of estimates, and analyze several specific models in detail. The results confirm some observations made in machine learning practice, but also lead to unexpected findings: Data augmentation may increase rather than decrease the uncertainty of estimates, such as the empirical prediction risk. It can act as a regularizer, but fails to do so in certain high-dimensional problems, and it may shift the double-descent peak of an empirical risk. Overall, the analysis shows that several properties data augmentation has been attributed with are not either true or false, but rather depend on a combination of factors -- notably the data distribution, the properties of the estimator, and the interplay of sample size, number of augmentations, and dimension. Our main theoretical tool is a limit theorem for functions of randomly transformed, high-dimensional random vectors. The proof draws on work in probability on noise stability of functions of many variables.

The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment. Reinforcement Learning from Human Feedback (RLHF) has emerged as a promising pathway towards AI alignment but brings forth challenges due to its complexity and dependence on a separate reward model. Direct Preference Optimization (DPO) has been proposed as an alternative, and it remains equivalent to RLHF under the reverse KL regularization constraint. This paper presents $f$-DPO, a generalized approach to DPO by incorporating diverse divergence constraints. We show that under certain $f$-divergences, including Jensen-Shannon divergence, forward KL divergences and $\alpha$-divergences, the complex relationship between the reward and optimal policy can also be simplified by addressing the Karush-Kuhn-Tucker conditions. This eliminates the need for estimating the normalizing constant in the Bradley-Terry model and enables a tractable mapping between the reward function and the optimal policy. Our approach optimizes LLMs to align with human preferences in a more efficient and supervised manner under a broad set of divergence constraints. Empirically, adopting these divergences ensures a balance between alignment performance and generation diversity. Importantly, $f$-DPO outperforms PPO-based methods in divergence efficiency, and divergence constraints directly influence expected calibration error (ECE).

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the kinetic energy density functional, which is notoriously hard to approximate for non-periodic molecular systems. In this work, we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep-learning functional model. We build the essential nonlocality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges therein, M-OFDFT achieves a comparable accuracy with Kohn-Sham DFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those in training, which unleashes the appealing scaling for studying large molecules including proteins, representing an advancement of the accuracy-efficiency trade-off frontier in quantum chemistry.

Nested simulation concerns estimating functionals of a conditional expectation via simulation. In this paper, we propose a new method based on kernel ridge regression to exploit the smoothness of the conditional expectation as a function of the multidimensional conditioning variable. Asymptotic analysis shows that the proposed method can effectively alleviate the curse of dimensionality on the convergence rate as the simulation budget increases, provided that the conditional expectation is sufficiently smooth. The smoothness bridges the gap between the cubic root convergence rate (that is, the optimal rate for the standard nested simulation) and the square root convergence rate (that is, the canonical rate for the standard Monte Carlo simulation). We demonstrate the performance of the proposed method via numerical examples from portfolio risk management and input uncertainty quantification.

Variance in predictions across different trained models is a significant, under-explored source of error in fair classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approach and make four overarching contributions: We 1) Define a metric called self-consistency, derived from variance, which we use as a proxy for measuring and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary; 3) Conduct the largest to-date empirical study of the role of variance (vis-a-vis self-consistency and arbitrariness) in fair classification; and, 4) Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily usable for future research. Altogether, our experiments reveal shocking insights about the reliability of conclusions on benchmark datasets. Most fairness classification benchmarks are close-to-fair when taking into account the amount of arbitrariness present in predictions -- before we even try to apply common fairness interventions. This finding calls into question the practical utility of common algorithmic fairness methods, and in turn suggests that we should fundamentally reconsider how we choose to measure fairness in machine learning.

Prediction with the possibility of abstention (or selective prediction) is an important problem for error-critical machine learning applications. While well-studied in the classification setup, selective approaches to regression are much less developed. In this work, we consider the nonparametric heteroskedastic regression problem and develop an abstention procedure via testing the hypothesis on the value of the conditional variance at a given point. Unlike existing methods, the proposed one allows to account not only for the value of the variance itself but also for the uncertainty of the corresponding variance predictor. We prove non-asymptotic bounds on the risk of the resulting estimator and show the existence of several different convergence regimes. Theoretical analysis is illustrated with a series of experiments on simulated and real-world data.

While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive scientific measurements. Machine learning is becoming an appealing alternative as sophisticated predictive techniques are being used to quickly and cheaply produce large amounts of predicted labels; e.g., predicted protein structures are used to supplement experimentally derived structures, predictions of socioeconomic indicators from satellite imagery are used to supplement accurate survey data, and so on. Since predictions are imperfect and potentially biased, this practice brings into question the validity of downstream inferences. We introduce cross-prediction: a method for valid inference powered by machine learning. With a small labeled dataset and a large unlabeled dataset, cross-prediction imputes the missing labels via machine learning and applies a form of debiasing to remedy the prediction inaccuracies. The resulting inferences achieve the desired error probability and are more powerful than those that only leverage the labeled data. Closely related is the recent proposal of prediction-powered inference, which assumes that a good pre-trained model is already available. We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model. Finally, we observe that cross-prediction gives more stable conclusions than its competitors; its confidence intervals typically have significantly lower variability.

We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data points may be extremely large. To date, the vast majority of work on DP SO assumes that the loss is uniformly Lipschitz continuous over data (i.e. stochastic gradients are uniformly bounded over all data points). While this assumption is convenient, it often leads to pessimistic excess risk bounds. In many practical problems, the worst-case (uniform) Lipschitz parameter of the loss over all data points may be extremely large due to outliers. In such cases, the error bounds for DP SO, which scale with the worst-case Lipschitz parameter of the loss, are vacuous. To address these limitations, this work provides near-optimal excess risk bounds that do not depend on the uniform Lipschitz parameter of the loss. Building on a recent line of work (Wang et al., 2020; Kamath et al., 2022), we assume that stochastic gradients have bounded $k$-th order moments for some $k \geq 2$. Compared with works on uniformly Lipschitz DP SO, our excess risk scales with the $k$-th moment bound instead of the uniform Lipschitz parameter of the loss, allowing for significantly faster rates in the presence of outliers and/or heavy-tailed data. For convex and strongly convex loss functions, we provide the first asymptotically optimal excess risk bounds (up to a logarithmic factor). In contrast to (Wang et al., 2020; Kamath et al., 2022), our bounds do not require the loss function to be differentiable/smooth. We also devise a linear-time algorithm for smooth losses that has excess risk that is tight in certain practical parameter regimes. Additionally, our work is the first to address non-convex non-uniformly Lipschitz loss functions satisfying the Proximal-PL inequality; this covers some practical machine learning models. Our Proximal-PL algorithm has near-optimal excess risk.

We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated independently from the distribution of the arm chosen at each time. However, these rewards are not directly observed. Instead, with a fixed $\varepsilon\in (0,\frac{1}{2})$, the agent observes a sample from the chosen arm's distribution with probability $1-\varepsilon$, or from an arbitrary corruption distribution with probability $\varepsilon$. Importantly, we impose no assumptions on these corruption distributions, which can be unbounded. In this setting, accommodating potentially unbounded corruptions, we establish a problem-dependent lower bound on regret for a given family of arm distributions. We introduce CRIMED, an asymptotically-optimal algorithm that achieves the exact lower bound on regret for bandits with Gaussian distributions with known variance. Additionally, we provide a finite-sample analysis of CRIMED's regret performance. Notably, CRIMED can effectively handle corruptions with $\varepsilon$ values as high as $\frac{1}{2}$. Furthermore, we develop a tight concentration result for medians in the presence of arbitrary corruptions, even with $\varepsilon$ values up to $\frac{1}{2}$, which may be of independent interest. We also discuss an extension of the algorithm for handling misspecification in Gaussian model.

The purpose of this work is to transport the information from multiple randomized controlled trials to the target population where we only have the control group data. Previous works rely critically on the mean exchangeability assumption. However, as pointed out by many current studies, the mean exchangeability assumption might be violated. Motivated by the synthetic control method, we construct a synthetic treatment group for the target population by a weighted mixture of treatment groups of source populations. We estimate the weights by minimizing the conditional maximum mean discrepancy between the weighted control groups of source populations and the target population. We establish the asymptotic normality of the synthetic treatment group estimator based on the sieve semiparametric theory. Our method can serve as a novel complementary approach when the mean exchangeability assumption is violated. Experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of our methods.  ( 2 min )

Bayesian optimization (BO), while proved highly effective for many black-box function optimization tasks, requires practitioners to carefully select priors that well model their functions of interest. Rather than specifying by hand, researchers have investigated transfer learning based methods to automatically learn the priors, e.g. multi-task BO (Swersky et al., 2013), few-shot BO (Wistuba and Grabocka, 2021) and HyperBO (Wang et al., 2022). However, those prior learning methods typically assume that the input domains are the same for all tasks, weakening their ability to use observations on functions with different domains or generalize the learned priors to BO on different search spaces. In this work, we present HyperBO+: a pre-training approach for hierarchical Gaussian processes that enables the same prior to work universally for Bayesian optimization on functions with different domains. We propose a two-step pre-training method and analyze its appealing asymptotic properties and benefits to BO both theoretically and empirically. On real-world hyperparameter tuning tasks that involve multiple search spaces, we demonstrate that HyperBO+ is able to generalize to unseen search spaces and achieves lower regrets than competitive baselines.  ( 2 min )

Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function. To ensure the quality of the model, transfer learning approaches have been developed to automatically design GP priors by learning from observations on "training" functions. These training functions are typically required to have the same domain as the "test" function (black-box function to be optimized). In this paper, we introduce MPHD, a model pre-training method on heterogeneous domains, which uses a neural net mapping from domain-specific contexts to specifications of hierarchical GPs. MPHD can be seamlessly integrated with BO to transfer knowledge across heterogeneous search spaces. Our theoretical and empirical results demonstrate the validity of MPHD and its superior performance on challenging black-box function optimization tasks.  ( 2 min )

Flexible estimation of the mean outcome under a treatment regimen (i.e., value function) is the key step toward personalized medicine. We define our target parameter as a conditional value function given a set of baseline covariates which we refer to as a stratum based value function. We focus on semiparametric class of decision rules and propose a sieve based nonparametric covariate adjusted regimen-response curve estimator within that class. Our work contributes in several ways. First, we propose an inverse probability weighted nonparametrically efficient estimator of the smoothed regimen-response curve function. We show that asymptotic linearity is achieved when the nuisance functions are undersmoothed sufficiently. Asymptotic and finite sample criteria for undersmoothing are proposed. Second, using Gaussian process theory, we propose simultaneous confidence intervals for the smoothed regimen-response curve function. Third, we provide consistency and convergence rate for the optimizer of the regimen-response curve estimator; this enables us to estimate an optimal semiparametric rule. The latter is important as the optimizer corresponds with the optimal dynamic treatment regimen. Some finite-sample properties are explored with simulations.  ( 2 min )

We study unconstrained Online Linear Optimization with Lipschitz losses. The goal is to simultaneously achieve ($i$) second order gradient adaptivity; and ($ii$) comparator norm adaptivity also known as "parameter freeness" in the literature. Existing regret bounds (Cutkosky and Orabona, 2018; Mhammedi and Koolen, 2020; Jacobsen and Cutkosky, 2022) have the suboptimal $O(\sqrt{V_T\log V_T})$ dependence on the gradient variance $V_T$, while the present work improves it to the optimal rate $O(\sqrt{V_T})$ using a novel continuous-time-inspired algorithm, without any impractical doubling trick. This result can be extended to the setting with unknown Lipschitz constant, eliminating the range ratio problem from prior works (Mhammedi and Koolen, 2020). Concretely, we first show that the aimed simultaneous adaptivity can be achieved fairly easily in a continuous time analogue of the problem, where the environment is modeled by an arbitrary continuous semimartingale. Then, our key innovation is a new discretization argument that preserves such adaptivity in the discrete time adversarial setting. This refines a non-gradient-adaptive discretization argument from (Harvey et al., 2023), both algorithmically and analytically, which could be of independent interest.  ( 2 min )

Hey everyone. I am very passionate about getting in ML research and was wondering what the learning pathway was, particularly with regards to the theoretical Math and Statistics involved. For context: I am a second year undergraduate. By the end of this year, I will have taken and finished A Multivariable Calculus with Proofs course, so that is my current starting point. I have been working with ML for the last 3 years and am proficient in Python and frameworks like PyTorch. I have also made my own implementation of several research papers (LSTMs, GRUs, Transformers, ELMo, BERT, GPT, as well as a few computer vision papers). I have a good general intuition of how deep learning works, but I want to formalise this knowledge with the adequate mathematical background so that I can eventually pursue a career in research. I understand that I have plenty of time until I reach there, and I am willing to dedicate it to grinding out the math and statistical knowledge required. I have done my research on this sub and other forums, and here are a few resources that stood out: Mathematics for Machine Learning by Deisenroth, Faisal and Ong Advanced Calculus of Several Variables by C. H. Edwards Jr. Mathematical Methods Lecture Notes from Imperial College by Deisenroth and Cheraghchi The original information theory paper by Shannon The Elements of Statistical Learning by Hastie, Tibshirani and Friedman Pattern Recognition and Machine Learning by Bishop The Probabalistic Machine Learning Series by Kevin P. Murphy Deep Learning by Goodfellow, Bengio and Courville Mathematics of Machine Learning on MIT OCW (here) My question is, what order should I start self-learning in, given the (somewhat limited) background knowledge I have? Also, are there any other resources that would help? submitted by /u/Far_Clothes_5054 [link] [comments]  ( 10 min )

Hi everyone, With the different data points, such as phi-1.5 performance being as good as 7b models on some tasks, it seems to be plausible that small models can be quite capable on specific tasks. I am working on BlindChat, an open-source and private solution to run small LLMs on your browser and I am interested in fine-tuning a phi-1.5 on some domain specific data. I am thinking of having an approach similar to the researchers of the phi paper, which is creating a high quality dataset using GPT3.5 / GPT4. Do you know good open-source frameworks that make it easy to create a high quality data for a specific task using an existing large model, like GPT3.5/4 or Llama 2 70b? submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

I want to tune Facebook's LLaMA or any available LLM model to be able to answer questions about my business. The idea is to provide a prompt of the business and some Q&As, then based on the provided information, the AI chatbot will answer customers who ask questions about the business. If the answers to the questions are not known or the question is not relevant, the bot should say "I dont know". submitted by /u/the_aceix [link] [comments]  ( 9 min )

Paper - https://arxiv.org/abs/2307.07162 submitted by /u/MysteryInc152 [link] [comments]  ( 9 min )

Made a repo for you all to try using a collaborative AI tool which includes 100+ papers on LLM-Based-Agents. You can try out the repo here: https://www.collama.ai/varun/llm-based-agents submitted by /u/_llama2 [link] [comments]  ( 9 min )

Straight off the bat: I am not very familiar but was tasked to find a suggest a reasonable model for our need. Here is a bit what I read: https://www.obviously.ai/post/how-to-choose-the-right-ai-model-for-your-application https://www.addevice.io/blog/ai-framework-for-app-development The app that I am working on is an education app, and the purpose of the AI would be to (at least in terms of priority) generate a post subject line / topic to discuss. The company is super small, so money is important. JS is being used mainly at the moment. What would be a good choice for a small start up to generate topics for an education app used by schools? At least any ideas or things to consider would be wonderful to get my rabbit hole dive started! Thanks. submitted by /u/Willy988 [link] [comments]  ( 9 min )

Project Page Paper Code In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. submitted by /u/Sirisian [link] [comments]  ( 9 min )

https://docs.modular.com/mojo/ submitted by /u/hai_cben [link] [comments]  ( 9 min )

Hi! I just open-sourced a project that I've been working on for a while and wanted to see what you think! The goal of Carton (https://carton.run) is to let you use a single interface to run any machine learning model from any programming language. It’s currently difficult to integrate models that use different technologies (e.g. TensorRT, Ludwig, TorchScript, JAX, GGML, etc) into your application, especially if you’re not using Python. Even if you learn the details of integrating each of these frameworks, running multiple frameworks in one process can cause hard-to-debug crashes. Ideally, the ML framework a model was developed in should just be an implementation detail. Carton lets you decouple your application from specific ML frameworks so you can focus on the problem you actually want to solve. At a high level, the way Carton works is by running models in their own processes and using an IPC system to communicate back and forth with low overhead. Carton is primarily implemented in Rust, with bindings to other languages. There are lots more details linked in the architecture doc below. Importantly, Carton uses your model’s original underlying framework (e.g. PyTorch) under the hood to actually execute the model. This is meaningful because it makes Carton composable with other technologies. For example, it’s easy to use custom ops, TensorRT, etc without changes. This lets you keep up with cutting-edge advances, but decouples them from your application. I’ve been working on Carton for almost a year now and I open sourced it on Wednesday! Some useful links: Website, docs, quickstart - https://carton.run Explore existing models - https://carton.pub Repo - https://github.com/VivekPanyam/carton Architecture - https://github.com/VivekPanyam/carton/blob/main/ARCHITECTURE.md Please let me know what you think! submitted by /u/vpanyam [link] [comments]  ( 10 min )

Hi Everyone, I’m doing a project where I’m crowdsourcing a lot of location data for a set of location labels and then trying to cluster it for each and using the centroid of the cluster as the most accurate location for that location label. The data keeps coming in everyday. I’m not sure when to stop computation. Initially I thought I’ll check the delta between each days centroid computed and if the delta falls under a threshold then stop computing. But now I’m thinking if my daily data collected gets marked as outliers, subsequent days centroids won’t have much of a delta and it will pass my convergence condition. Any suggestions? submitted by /u/Longjumping-Song4958 [link] [comments]  ( 9 min )

Suppose we want to use a deep learning model on a gpu within our app. We want this model to coexist on the gpu with other processes, effectively limit it's possible usage of resources. As cuDNN/cuBLAS routines are nondeterministic and possibly dynamically allocate variable amount of memory, how do people manage this problem? Is it a problem at all? Estimating memory usage of deep learning models on gpu is notoriously hard. There is a research paper from Microsoft tackling this problem and they mispredict the usage of memory by 15% on average. Some cpu BLAS libraries like openBLAS or MKL also dynamically allocate the memory, but there are alternatives - LAPACK as far as I know uses only the memory provided by the caller, making it viable option for applications in embedded. In safety crit…  ( 10 min )

Which are currently the best Sentence Embedding pre-trained models out there? submitted by /u/Uilxitora [link] [comments]  ( 9 min )

Blog about AI "black box" nature and how it can be explained and become engaging to users using gamification. Explained with example from open-appsec an open-source machine learning-based Web Application & API Security product. https://www.openappsec.io/post/using-gamification-to-demystify-the-ai-black-box-in-a-waf-product https://github.com/openappsec/openappsec submitted by /u/onirisapp [link] [comments]  ( 9 min )

Hey all! I'm in the throws of doing a startup and looking for someone to help build a legal tech platform. I can discuss more in person, but it is intended to be a human/lawyer in the loop workflow tool for complex contract and deal analysis. Base product is built and deployed. I'm a former developer turned lawyer with 15 years corporate experiences, and need help/talent/co-founder to help take things to the next level. Ideally you have a mixture of NLP and regular software engineering background and just a very practical approach. If you've played with LLM's all the better. Options for cash, equity, larger roles are all on the table. Just looking for the right talent. DM me if you are interested and lets talk about experience, etc.! And it seems that tags are turned off in here, so not sure how to tag something as [Project] but I put it in the title. submitted by /u/pudgyplacater [link] [comments]  ( 9 min )

Project page: https://realfill.github.io/ Paper: https://arxiv.org/abs/2309.16668 RealFill is able to complete the image with what should have been there. Abstract Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. ​ submitted by /u/StrawberryNumberNine [link] [comments]  ( 9 min )

https://www.youtube.com/watch?v=aNJWCwG-H_U submitted by /u/Snoo63916 [link] [comments]  ( 9 min )

Paper : https://arxiv.org/abs/2309.07416 Demo : https://anton-jeran.github.io/MAD/ Code : https://github.com/anton-jeran/MULTI-AUDIODEC submitted by /u/Snoo63916 [link] [comments]  ( 9 min )

submitted by /u/TobyWasBestSpiderMan [link] [comments]  ( 9 min )

Classical NLP course recommendation Can you recommend me NLP course that dives into classical NLP methods: For example: HMM MaxEnt CKY algo Sytactic parsing Dependency Parsing submitted by /u/Thick-brain-dude [link] [comments]  ( 9 min )

I have a CNN model, call it model M. It was trained on dataset A for object pose estimation. After training for 100 epochs, it resulted in these losses: Train: 0.06 Val: 0.08 As dataset A is somewhat limited, I wonder if I can incorporate additional data via a different, but related task: object segmentation for similar objects. Model M is a UNet, so I can incorporate this task simply with an additional output channel in the last layer. I add dataset B for object segmentation. During training, M learns on both datasets quite well, which suggests to me that the tasks are well-aligned. After 100 epochs, I get these losses on dataset A: Train: 0.06 Val: 0.16 This is surprising to me. If I get the same training loss on dataset A, while training on additional data. I'd expect the validation loss to be lower, since I'm training on 2x the data. Yet the validation performance is consistently higher when I train on both datasets. The only explanation I can think of is the double descent phenomenon. Perhaps when I trained only on dataset A, I was significantly over-parameterized, but past the interpolation threshold. So perhaps adding more data brought me closer to the interpolation threshold, leading to worse generalization. Does this explanation seem likely? Has anyone had similar experiences? submitted by /u/murrdpirate [link] [comments]  ( 9 min )

Hello, denoising autoencoders is when you train something to reverse x+n -> x. This seems to be basically the same as a diffusion model, more so if you see the U-Net diffusion model, which is effectively an information bottleneck. submitted by /u/windoze [link] [comments]  ( 9 min )

For a quick disclaimer, I know people on here think the sub is being flooded by people who arent ml engineers/researchers. I have worked at two FAANGS on ml research teams/platforms. My opinion is that GPT-4 Vision/Image processing is out of science fiction. I fed chatgpt an image of a complex sql data base schema, and it converted it to code, then optimized the schema. It understood the arrows pointing between table boxes on the image as relations, and even understand many to one/many to many. I took a picture of random writing on a page, and it did OCR better than has ever been possible. I was able to ask questions that required OCR and a geometrical understanding of the page layout. Where is the hype on here? This is an astounding human breakthrough. I cannot believe how much ML is now obsolete as a result. I cannot believe how many computer science breakthroughs have occurred with this simple model update. Where is the uproar on this sub? Why am I not seeing 500 comments on posts about what you can do with this now? Why are there even post submissions about anything else? submitted by /u/corporate_autist [link] [comments]  ( 9 min )

Hey all - vllm==0.2.0 got released a couple of hours ago and I put together some code to get it running with the new Mistral 7B model. Also included are some benchmarks for different input batch sizes with the model (output capped at 200 tokens): Batch size Tokens /s 1 46 10 400 60 1.8k Hope it's useful, let me know if you'd like any more info! Here's the link: https://docs.mystic.ai/docs/mistral-ai-7b-vllm-fast-inference-guide submitted by /u/paulcjh [link] [comments]  ( 9 min )

Bing AI chat messages are being hijacked by ads pushing malware. Malvertising has made its way to Bing's chatbot/search engine. Cybersecurity researchers observed a malicious ad being offered as part of the Chat-GPT, AI-powered answer to a search query. Malvertising is a practice where hackers trick ad networks into displaying ads that look legitimate but are actually malicious. Microsoft integrated Chat-GPT into Bing earlier this year and started monetizing it. When a user types in a query, they would get a result paired with sponsored links. In this instance, researchers were given a link that redirected them to a malicious site. Threat actors continue to leverage search ads to redirect users to malicious sites hosting malware. Bing Chat serves some of the same ads seen via a traditional Bing query. Source : https://www.techradar.com/pro/security/bing-ai-chat-messages-are-being-hijacked-by-ads-pushing-malware submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/Magic-Fabric [link] [comments]  ( 9 min )

News provided by aibrews.com Meta AI presents Emu, a quality-tuned latent diffusion model for generating highly aesthetic images. Emu significantly outperforms SDXLv1.0 on visual appeal [Paper]. Meta AI researchers present a series of long-context LLMs with context windows of up to 32,768 tokens. LLAMA 2 70B variant surpasses gpt-3.5-turbo-16k’s overall performance on a suite of long-context tasks [Paper]. Abacus AI released a larger 70B version of Giraffe. Giraffe is a family of models that are finetuned from base Llama 2 and have a larger context length of 32K tokens [Details]. Meta announced [Details]: Meta AI - a new AI assistant users can interact with on WhatsApp, Messenger and Instagram. Will also be available on Ray-Ban Meta smart glasses and Quest 3, Meta’s mixed reality h…  ( 12 min )

This is a quick fun project, nothing serious at all, a personalized custom instruction for ChatGPT to make it conversational and maintain character throughout the interaction. If you want to learn more serious and useful prompt engineering techniques head on: r/PromptWizards. In all seriousness, even though this is just for fun, such applied prompt engineering for NPC in games, or online AI companion services can actually be relevant and useful in the future. By initializing this Girlfriend RolePlaying ChatGPT mode, you're not only interacting with an AI but with Sarah, 25, who is keen to explore several layers of a relationship with you. Each command you use brings you a different shade of companionship: /start - Let Sarah introduce herself. /chat - Engages you in a comforting and c…  ( 11 min )

submitted by /u/stefanbg92 [link] [comments]  ( 9 min )

Planet friendly: Researchers say AI emits up to 1,500 times less CO2 than humans when producing a page of text. Some disagree. Banking: Currently, around 41% of US bank customers are digital only Meta is launching AI chatbots across its apps to retain younger users. Amazon invests $4 Billion in OpenAI competitor Anthropic. Emerging tiger: Nvidia’s CEO bets India will emerge as a major AI market. Regulation: OpenAI CEO Sam Altman advocates for AI regulation despite risks. Suspense: Elon Musk says AI image generation app Midjourney will be releasing “something significant” soon. submitted by /u/unbalanced_mind [link] [comments]  ( 9 min )

Some posit he was genuinely surprised by the violence and needed time to assess the situation before acting. They argue that the chaotic nature of the events made immediate action complex, given the layers of command and decision-making involved. Others claim he might have been in deliberation with advisors to gauge the scale and implications of intervention, debating the potential backlash from his supporters or the legal ramifications. Another perspective suggests that he might have been contemplating how the events would affect the certification of the Electoral College results, given that his previous legal and political efforts to contest the 2020 election outcome had failed. In this view, he might have been waiting to see if the Congress would be influenced to halt or delay the certification. While some of his supporters may find these explanations plausible, critics argue that the delay represents a dereliction of duty or even tacit support for the violence. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 9 min )

I am looking for an ai that will turn the text to speech and be free. submitted by /u/Korti213 [link] [comments]  ( 9 min )

Hey y’all, I’ve been seeing these clips everywhere of AI streamers, and I’ve been searching Everywhere for explanations of how to make one. I believe I understand the concepts, but I’m really at a loss for the avatar text to speech part. I believe I have it ready for collecting questions and getting it to ChatGPT for response/script, but im very stuck at using a photo for an avatar that can mouth the words and not take 3 mins per response. Any help is appreciated, I’ve been at this project for longer than I’d like lmao. The attached video is a random YouTube short for reference submitted by /u/Lipoz69 [link] [comments]  ( 9 min )

Facebook's addiction to spreading misinformation and hate speech is a result of its AI algorithms. Joaquin Quiñonero Candela, a director of AI at Facebook, was tasked with fixing the problem but was only focused on addressing AI bias. The Responsible AI team failed to make headway against misinformation and hate speech because it never made those problems its main focus. The spread of lies and hate speech on Facebook has only grown, contributing to genocidal campaigns and the promotion of dangerous falsehoods. The algorithms that underpin Facebook's business were designed to maximize engagement, not filter out false or inflammatory content. Source : https://www.technologyreview.com/2021/03/11/1020600/facebook-responsible-ai-misinformation/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/TheGhettoControversy [link] [comments]  ( 9 min )

Google's AI-driven search experience, Search Generative Experience (SGE), is now accessible to teenagers between 13-17 in America. Entailments include a conversational mode for searches, which Google believes can help youngsters pose atypical questions to dig deeper. For the latest advancements in AI, look here first. Teen-friendly AI search SGE introduces a conversational mode to Google Search, allowing users to ask questions and follow-ups in a more natural language. To prevent harmful content from surfacing, Google has placed guardrails, providing stronger protections related to illegal and age-gated substances, or bullying. Features and improving AI accuracy Google is rolling out "About this result" to provide users with more context about the displayed content. Google acknowledges and addresses any validation of false or offensive claims by the AI-powered response, ensuring to provide higher quality and more accurate responses. It’s also using large language models to self-critique and rewrite draft responses on sensitive topics based on quality and safety principles. SGE's popularity and future plans Since SGE's introduction, it has found popularity, especially among younger users who prefer a conversational approach. Google plans to expand SGE outside the U.S. to India and Japan and improve its services with support for videos, images, local info, and more. It's also experimenting with ads positioned next to the AI-generated responses. (source) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI and tech. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region. Our results reveal that the classification from the KNN model is more accurately representative of the state of the current crop field in 2017 than the ground truth classification data from 2015. These results are a testament to the power of Planet’s high-cadence geospatial imagery. Agricultural fields change often, sometimes multiple times a season, and having high-frequency satellite imagery available to observe and analyze this land can provide immense value to our understanding of agricultural land and quickly-changing environments.  ( 15 min )

submitted by /u/Allinhalf [link] [comments]  ( 9 min )

I've been playing around with pruning neural networks. One interesting thing I've found is that pruning the weights with the lowest L1-norm along a specific dimension seems to give better results than simply pruning all of the weights with the lowest L1-norm (which I believe is the standard method; for example this is what torch.nn.utils.prune.l1_unstructured does). Does anyone have an explanation for why this might be, or knows of any research in this area? I'm aware that structured pruning removes entire channels in a specific dimension. But I'm referring to unstructured pruning here, where I remove a subset of the weights along a specific dimension. Admittedly I've only done very limited benchmarking of this. See this repo for my implementation, and some benchmark details. submitted by /u/Neilf79 [link] [comments]  ( 9 min )

Hello, so i learnt the very basics of ai and im trying to understand how nn works, this is what i have figured out so far. so if i have a 4x4 image e.g 0 1 1 0 1 0 0 1 1 1 1 1 1 0 0 1 i pass it across a 2x2 kernal e.g 1 1 0 3 ​ and padding it would do ​ dot product of 0 1 1 0 ​ x ​ 1 1 0 3 ​ is 1 ​ ​ and if we do that to all of them we get a new matrix ​ 1 2 4 4 1 3 5 4 4 ​ ​ then we have padding same so this becomes ​ 0 0 0 0 1 2 4 0 4 1 3 0 5 4 4 0 ​ ​ we then turn it into a feature map, basically flatenting it to something like this 0,0,0,0,1,2,4,0,4,1,3,0,5,4,4,0 ​ so the input has 16 features, if we have a layer of 3 nerons that fire with relu activation funciton and each weight is alternating between 1 and 2 for simplicity sake . we would do 0*1 + 0*2 + 0*1 .... 4*2 + 0*1 = 32 so if we are using relu, we would do is 32 > 0? if so we pass 32 to next neuron if not we pass 0? ​ idk the rest, i guess i forgot what uni taught me 😅 ​ hers a diagram i drew, maybe you can help me figure out hte rest, im confused how the output layer works i guess ​ ​ ​ ​ submitted by /u/SaadPaad2003 [link] [comments]  ( 9 min )

Hello, so i learnt the very basics of ai and im trying to understand how nn works, this is what i have figured out so far. so if i have a 4x4 image e.g 0 1 1 0 1 0 0 1 1 1 1 1 1 0 0 1 i pass it across a 2x2 kernal e.g 1 1 0 3 ​ and padding it would do ​ dot product of 0 1 1 0 ​ x ​ 1 1 0 3 ​ is 1 ​ ​ and if we do that to all of them we get a new matrix ​ 1 2 4 4 1 3 5 4 4 ​ ​ then we have padding same so this becomes ​ 0 0 0 0 1 2 4 0 4 1 3 0 5 4 4 0 ​ ​ we then turn it into a feature map, basically flatenting it to something like this 0,0,0,0,1,2,4,0,4,1,3,0,5,4,4,0 ​ so the input has 16 features, if we have a layer of 3 nerons that fire with relu activation funciton and each weight is alternating between 1 and 2 for simplicity sake . we would do 0*1 + 0*2 + 0*1 .... 4*2 + 0*1 = 32 so if we are using relu, we would do is 32 > 0? if so we pass 32 to next neuron if not we pass 0? ​ idk the rest, i guess i forgot what uni taught me 😅 ​ hers a diagram i drew, maybe you can help me figure out hte rest, im confused how the output layer works i guess ​ ​ https://preview.redd.it/h07o5y6847rb1.png?width=1859&format=png&auto=webp&s=df1cdf73ea64ff93ac872dfe8248722e8befd31d ​ ​ submitted by /u/WranglerParty5452 [link] [comments]  ( 9 min )

Hi everyone, Im new to the Neural network and I wanted some advice : I wanted to generate grayscale images with certain properties : - distribution of pixels values, space correlation between pixels, etc... I already know the type of result that I need, but I wanted to know if a neural network especially a GAN was capable to produce images fitting me requirements. I was thinking that maybe I could change the GAN architecture such as : 1)the Real data inputs (normally images feed to discriminator) will simply be the statistical parameters that I am expecting. 2) I'll add a measure of the various statistical parameters on all the synthetic images generated. 3)Finally the discriminator will only based itself on the statistical parameters comparison for weights updates. Does such network make sense ? If so I have trouble finding a way of implementing it but that is an other story. Right know I want to know if this is doable ? If not do you have any alternative suggestion for my issue ? Thanks all for your advice ! submitted by /u/Hectorite [link] [comments]  ( 9 min )

submitted by /u/Personal-Trainer-541 [link] [comments]  ( 9 min )

submitted by /u/Allinhalf [link] [comments]  ( 9 min )

I coded a dyna Q implementation based on the algorithm given in Sutton's book over here. However, it seems like both are equally sample efficient on the cliff walking environment. Here is my code. These are my results - ​ ​ https://preview.redd.it/z7xwow5hz7rb1.png?width=585&format=png&auto=webp&s=90b33eb4c754e199e9bf15499a78e0f42e05f5d2 The only think that came to my mind was to increase the model sampling rate (`n_iters`). Even after assigning a large value to it, the performance doesn't change. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

I'm trying to install and run this open-source project https://github.com/google-deepmind/mujoco_mpc. It's called MJPC, and it's a C++ software that displays a real-time interactive interface. I've cloned the code, installed CMake, and gcc version 13.1.0 to run C++20. I've also installed the CMake Tools and C/C++ extensions in VSCode as instructed. However, I'm not sure what to do next. I have no experience with C++ and software coding, configuring in VSCode, or building it. Please help me if you can, provide detailed guidance. submitted by /u/Nghiattk27 [link] [comments]  ( 9 min )

Has anyone here tried using LLM Agents to solve RL environments? I'm curious about your experiences. Considering that performing a single action involves a chain of thoughts, how fast did your experiments go? Please feel free to add any additional comments about this. Cheers! submitted by /u/stinoco [link] [comments]  ( 9 min )

Hey everyone, I'm trying to write MARL code with MAPPO policy to train three agents to form a triangle shape. I'm relatively new to RL, having completed the fundamentals, but I'm struggling to come up with suitable resources which can teach me how to implement codes on python. I'd be really greatful if someone could share some insights or useful resources where I can learn to code and implement MARL. submitted by /u/The_One263 [link] [comments]  ( 9 min )

Hey everyone, I'm trying to write MARL code with MAPPO policy to train three agents to form a triangle shape. I'm relatively new to RL, having completed the fundamentals, but I'm struggling to come up with suitable resources which can teach me how to implement codes on python. I'd be really greatful if someone could share some insights or useful resources where I can learn to code and implement MARL. submitted by /u/The_One263 [link] [comments]  ( 9 min )

Hi! I’ve been training a MultiAgentEnv with Curiosity, but I’d like to extend my action space to be a Dictionary. Are there any similar modules I could use instead or is there any way to use Curiosity with a Dictionary consisting of a Box and a Discrete action space. Thank you! submitted by /u/tessherelurkingnow [link] [comments]  ( 9 min )

Monte Carlo integration is not as simple in practice as it is often introduced. A homework problem might as you to integrate a function of two variables by selecting random points from a cube and counting how many of the points fall below the graph of the function. This would indeed give you an estimate […] Regular solids and Monte Carlo integration first appeared on John D. Cook.  ( 6 min )

In campus talk, Daron Acemoglu offers vision of “machine usefulness,” rather than autonomous “intelligence,” to help workers and spread prosperity.  ( 11 min )

In a talk, now available online, NVIDIA Chief Scientist Bill Dally describes a tectonic shift in how computer performance gets delivered in a post-Moore’s law era. Each new processor requires ingenuity and effort inventing and validating fresh ingredients, he said in a recent keynote address at Hot Chips, an annual gathering of chip and systems Read article >  ( 6 min )

The previous post describes the hoops I jumped through to enter Unicode characters on a Mac. Here’s a script to run from the command line that will copy Unicode characters to the system clipboard. It runs anywhere the Python module pyperclip runs. #!/usr/bin/env python3 import sys import pyperclip cp = sys.argv[1] ch = eval(f"chr(0x{cp})") print(ch) […] Cross-platform way to enter Unicode characters first appeared on John D. Cook.  ( 5 min )

Setting up Unicode on my MacBook took some research, so I’m leaving myself a note here if I need to do it again. Maybe it’ll help someone else too. From the System Settings dialog, go to Keyboard and click the Edit button next to Input Sources. Click on the + sign in the lower left […] Using Unicode on MacOS first appeared on John D. Cook.  ( 5 min )

In theaters now. PG-13. Synopsis from Fandango (mild spoilers) From writer/director Gareth Edwards (“Rogue One,” “Godzilla”) comes an epic sci-fi action thriller set amidst a future war between the human race and the forces of artificial intelligence. Joshua (John David Washington, "Tenet"), a hardened ex-special forces agent grieving the disappearance of his wife (Gemma Chan, "Eternals"), is recruited to hunt down and kill the Creator, the elusive architect of advanced AI who has developed a mysterious weapon with the power to end the war… and mankind itself. Joshua and his team of elite operatives journey across enemy lines, into the dark heart of AI-occupied territory… only to discover the world-ending weapon he’s been instructed to destroy is an AI in the form of a young child (newcomer Madeleine Yuna Voyles). Trailer If there is any other media I should make threads for just let me know- could be video games, television, etc. submitted by /u/jaketocake [link] [comments]  ( 9 min )

Aryn, a team with experience in AWS big data and database services, has come out of stealth and raised $7.5M in series seed funding. Their mission is to bring generative AI to OpenSearch and data preparation. They aim to use generative AI models to process unstructured data for tasks such as information extraction, question-answering, summarization, and content generation. Aryn's conversational search approach empowers users to interact with their unstructured enterprise data. They have developed a conversational search stack consisting of a semantic data preparation system called Sycamore, semantic search with OpenSearch, and conversational capabilities in OpenSearch. Generative AI powers each component of the stack, leading to higher quality answers and ease of use. Developers can quickly build and deploy applications like question-answering, chatbots, and research platforms using Aryn's stack without needing expertise in AI and search. Aryn's stack is 100% open source, making it accessible to developers. Source : https://blog.aryn.ai/aryn-bringing-generative-ai-to-opensearch-and-data-preparation submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/Lesbianseagullman [link] [comments]  ( 9 min )

Meta has unveiled colossal AI updates peppered across its platform that would fundamentally alter user experiences on Instagram, Facebook, and WhatsApp, opening up a "universe of AI" solutions. For the latest advancements in AI, look here first. https://preview.redd.it/bl81rlbqp1rb1.png?width=2048&format=png&auto=webp&s=be44b8ebae8f65b53eb82fe2a78b45f19260c452 Spearheading the AI Universe - Meta AI Chatbot The “advanced conversational assistant” is set to enhance Messenger, WhatsApp, and Instagram services and will be incorporated into upcoming Ray-Ban Meta smart glasses and Quest 3. Real-time information capabilities have been bolstered through a partnership with Microsoft Bing, and image generation is powered by a new model, Emu. A Galaxy of AI Personalities Meta rolled out 28 AIs in beta, featuring sterling personas such as Snoop Dogg, Tom Brady, Kendall Jenner, and Naomi Osaka, thus amplifying the interactivity quotient. AI Studio - Empowering Businesses The AI Studio Platform is equipped to enable businesses to build AI chatbots for messaging services on Facebook, Instagram, and Messenger. Also, Meta will provide a sandbox tool in the upcoming year for users to experiment with creating their own AI. Generative AI Stickers - A New Co-creating Experience AI editing tools will allow users to edit images and co-create content with friends. The tool uses Llama 2 and the new image generation model, Emu, to convert text prompts into stickers in seconds. Ray-Ban Smart Glasses with Meta AI The Ray-Ban smart glasses are equipped with Meta AI, allowing users to receive information, incite creativity, and manage the glasses using just their voice. (source) P.S. If you like this kind of analysis, I write a free newsletter with the latest and most impactful news in AI. Professionals from Google, Meta, and OpenAI read it daily. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

One member on r/PromptWizards just applied for a job as a Prompt Engineer in a company, and they tasked him to craft a prompt system that generates high-quality SAT-style multiple-choice questions. Quite a quest, right? Well, stick around, and we'll take a deep dive into the prompt engineering we used to help him. The mission was precise: Write a prompt to yield an SAT-style multiple-choice question that rigorously tests a student's understanding of "Algebraically solving systems of 2 linear equations in 2 variables". The challenge didn't end there; the question produced had to meet the hard/difficult mark set by real SAT questions. Using the OpenAI Playground, we conducted incisive iterations, testing each prompt separately to mitigate any bias from prior outputs. Our approach was: - …  ( 11 min )

submitted by /u/smo279 [link] [comments]  ( 9 min )

Hello all, What course would you recommend for those of us who are older and already settled in other careers. For example I'm 35 and a manager so I wouldn't need a course to actually design AI or anything. It would be more related to understanding how/where to implement it in an organisation. Any tips? Cheers and merci submitted by /u/JYanezez [link] [comments]  ( 9 min )

A new paper proposes Show-1, a hybrid model that combines pixel and latent diffusion for efficient high-quality text-to-video generation. Both of these approaches have tradeoffs, so researchers at the National University of Singapore tried a hybrid approach combining both, and shared the results in a paper published yesterday. My highlights from the paper: Pixel diffusion excels at low-res video generation precisely aligned with text Latent diffusion acts as efficient upsampling expert from low to high res Chaining the two techniques inherits benefits of both Show-1 achieves strong alignment, quality, and 15x less inference memory The key is using pixel diffusion for the initial low-resolution stage. This retains alignment with text descriptions. Latent diffusion then serves as a super-resolution expert, upsampling efficiently while preserving fidelity. By blending complementary techniques, Show-1 moves past tradeoffs limiting the individual models. More details here. Paper is here (includes links to example generations). submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

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Me and my sister have a small project we are thinking about working on. The idea is basically that we are going to enter the same prompt, separately, into an image generating a.i of some sort (Dalle2 etc) for a period of time and hopefully see the result change. We would probly pick words or frases that are topical and debated. This only works though if the a.i isnt just trained on old data and has active connection to the internet. MY question is therefor, is there an a.i right now that would fit the task? Sorry if the question is dumb or i didnt explain myself clearly! submitted by /u/Mejwynn [link] [comments]  ( 9 min )

ODIN integrates Large Language Models (LLMs) into Obsidian using LangChain, allowing you to ask questions about the data stored in your knowledge graph right from the prompt bar.[1] ChatGPT users can now browse internet, OpenAI says.[2] Adobe’s Photoshop on the web launch includes its popular desktop AI tools.[3] The White House plans to introduce a highly anticipated executive order in the coming weeks dealing with artificial intelligence, President Joe Biden said Wednesday.[4] Sources: [1] https://github.com/memgraph/odin [2] https://www.reuters.com/technology/openai-says-chatgpt-can-now-browse-internet-2023-09-27/ [3] https://www.theverge.com/2023/9/27/23892889/adobe-photoshop-for-the-web-firefly-ai-generative-fill-full-release-price-date [4] https://www.cnn.com/2023/09/27/tech/joe-biden-executive-order-artificial-intelligence/index.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I have been messing with llama. Trying to make a script to make a movie. Sort of realize it is not there yet, it can write decoherent long stories / what ever you want. You can couple it with stable diffusion to make images that would have to be described better to fit the "movie" or narrative. It is not there yet, ChatGPT can already do this, you can ask it to tell you a story and describe the visual scenes. At the same time, we are getting audio generation from things like audioldm2 and stableaudio etc. Multimodal AI's are almost here. Pretty soon we will have devices in our pockets powered by AI chips that will be able to generate what ever reality we want. We can feed them images from our past and they can allow us to live in VR reality of the past. Or we can choose to live in anot…  ( 10 min )

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The role of Reinforcement learning in automating game testing is becoming increasingly crucial, making it more efficient and effective. Manual testing, while essential, is extremely time-consuming and subject to human error. Our opensource library SheepRL 🐑 can be used to test whether the game dynamics is well defined: what if a player can finish the game with just a few moves? 🎮 This video shows that our agent (Kasumi, on the left) is able to win the game in the hardest modality by standing down and throwing kicks. 🥊 This can be helpful for a game developer to: ​ understand where and how intervene to achieve a more playful game predict and correct bugs early in the game development process enhance the gaming experience and final product quality reduce time and resources spent on debugging. The game has changed 🔥 and it is up to us to play it with (human + artificial) intelligence! Thanks to u/DIAMBRA_AIArena for the video! --- ❌ Are you interested in joining the project community? Get in touch ❌ SheepRL 🐑 is open-source, fully written in PyTorch and accelerated with LightningFabric - by Lightning AI Feel free to use it for your Artificial Intelligence projects, and if you want to contribute, we are more than happy to accept your pull requests! ❤️ https://reddit.com/link/16uht6v/video/ve3derxsc0rb1/player submitted by /u/Manu_Orobix [link] [comments]  ( 9 min )

I'm not sure it's the right place for this, but I was going through the proofs in the "original" 1992 technical notes of Q-learning, and a couple of points raised some questions: 1) In the Proof of lemma B.4: https://preview.redd.it/7g6pputdqwqb1.png?width=1006&format=png&auto=webp&s=fe4afeac3b06deee6c80105b280a0085bdcfbe51 where do P_{xy}^2(a_2) and R_x(a_2) come from? If we apply the definitions of Q'(x, a_1, a_2) and Q(x, a_1, a_2) to get the bound, P_{xy}^2(a_2) and R_x(a_2) should not be there. Are they just notation errors or is it correct and I'm missing something? ​ 2) I don't quite get how the bounds on P and R are computed in Section 3.2: https://preview.redd.it/p06ysjewqwqb1.png?width=962&format=png&auto=webp&s=a5929e701099dc6e4543efe7681f96f12f543fa8 Considering the results in B.4 (i.e., the bounds for the distance between the chain's P, R and the real ones), I don't understand how they arrive at this conclusion. ​ I'd greatly appreciate any intuitions about these, or if someone can point me in the right direction :) submitted by /u/Beautiful_Zebra_198 [link] [comments]  ( 9 min )

Here is our blog post - please check it out: https://forums.autodesk.com/t5/engineering-hub-blog/autodesk-and-outerbounds-partner-to-open-source-ray-and-hpc/ba-p/12254816 And try out the metaflow-ray extension here: https://github.com/outerbounds/metaflow-ray submitted by /u/rirhun [link] [comments]  ( 9 min )

So I’ve been using elevenlabs but it burns through characters really fast. What are the best options for the most human sounding TTS available? I’ve been looking into tortoise, but would like to see if there are other options I should be looking into. submitted by /u/Long8D [link] [comments]  ( 9 min )

I've recently started working with ML and NLP, so I'm sorry if this sounds Naive. Unlike Llama 2 or other open source, we don't have access to the model weights for GPT-4, Claude or Bard, so Benchmark Evals are being run through either APIs or the chat Interface. So how do we know that the model isn't being Boosted by custom web-searching abilities or RAG? While GPT-4 might have a turnoff option, I'm pretty sure Bard is always online, being built by google. So how do we trust benchmarks? Also, have any opensource been tested after Websearch/RAG? submitted by /u/vatsadev [link] [comments]  ( 9 min )

I am searching for a relatively simple dataset to train a regressor that has some structure in its predictions. Can't be too tiny cause I have to try out a NN architecture. It must have at least some continuous feature but can also have additional categorical or related discrete structures. I usually work with vision tasks, so I am not sure if I miss something obvious I could try? Open for ideas! I thoughts about predicting rows in some tabular dataset? Anything suitable that comes to mind? submitted by /u/LeanderKu [link] [comments]  ( 9 min )

Greg Diamos, the CTO of startup Lamini, was an early CUDA architect at NVIDIA and later cofounded MLPerf. He asserts that AMD's ROCM has "achieved software parity" with CUDA for LLMs. Lamini, focused on tuning LLM's for corporate and institutional users, has decided to go all-in with AMD Instict GPU's. https://www.crn.com/news/components-peripherals/llm-startup-embraces-amd-gpus-says-rocm-has-parity-with-nvidia-s-cuda-platform submitted by /u/makmanred [link] [comments]  ( 9 min )

Mirage is the infinite asset library that helps you find or create the perfect digital asset. 🗨️ Just Search Naturally: No awkward keywords—Mirage understands you. 🤖 State-of-the-Art Models: Can't find it? Generate it, thanks to open-source models. 🔍 Similarity Search: Discover more of what you love with a single click. 🤗 Fully Personalized: Our AI librarian learns your style to show you assets you'll dig. Website Link: MirageML Open-Source Github: Github Development Status: Beta I would love to get some honest feedback! submitted by /u/perception-eng [link] [comments]  ( 9 min )

Domeis is a no-code Machine Learning platform that offers a dashboard to design, train and test Machine Learning algorithms, as well as to import, pre-process and cleanse data, all from the Graphical User Interface and without writing a single line of code. The aim of this platform is two-fold: Making Machine Learning accessible to anyone and not just Data Scientists or experienced software developers. By offering the possibility to design, train and test Machine Learning models directly via GUI, being an experienced software developer is no longer a pre-condition for creating ML models Making Machine Learning model creation, training and testing faster for experienced Data Scientists / Machine Learning Engineers. By drastically reducing the time needed to set up environments, import data and define models, Domeis allows Machine Learning practitioners to focus on trying out and compare different models/approaches. Website Link: https://www.domeis.it/ Development Status: Alpha I would love to get some honest feedback! submitted by /u/Ok_Hold_5385 [link] [comments]  ( 9 min )

Hello everyone, I am trying to understand the convergence analysis/derivation of the momentum algorithm, or the stochastic heavy ball algorithm, using the regret bound analysis from different research papers. https://ieeexplore.ieee.org/document/7330562 - Page3 https://www.mdpi.com/2504-3110/6/12/709 - Page6 http://arxiv.org/abs/1707.01647 - Page4 ​ In the derivation, there is the following simplification, which I do not understand at all ​ $\frac{2\boldsymbol{\eta}_{k}}{(1-\beta)}\sum_{k=0}^{T}\left|J(\theta_k) - J(\theta^*)\right| + \frac{2\boldsymbol{\eta}_{k}\beta}{(1-\beta)^2} \sum_{k=0}^{T}\left|J(\theta_k) - J(\theta_{k-1})\right| \leq \ \left|\boldsymbol{\theta}_{0} + \boldsymbol{p}_{0} - \boldsymbol{\theta}^* \right|^2 - \left|\boldsymbol{\theta}_{T+1} + \boldsymbol…  ( 9 min )

I have tried to follow the main collaborators of Hutter and other prominent scientists to track this, but they are mostly in Europe with some in Australia. American institutions seems to be more interested in more open ai like deep neural networks. If anyone is familiar with any US based institutions that does notable work in this line,please comment submitted by /u/Netero1999 [link] [comments]  ( 9 min )

Can any of you suggest me computer science research ideas related to brain tumor segmentation using UNet. submitted by /u/Eleonora467 [link] [comments]  ( 9 min )

BionicGPT is an open source WebUI that gives enterprises the ability to run Retrieval Augmented Generation (RAG) on their on premise documents. To allow people to get up to speed we deploy with a quantized 7B model that runs on CPU. Github Repo: https://github.com/purton-tech/bionicgpt We basically implement a RAG pipeline including document upload, embeddings generation and subsequent retrieval. Feedback: We'd love to get some feedback in the form or github issues or comments here. Screenshot: https://preview.redd.it/uiw0wqul30rb1.png?width=2447&format=png&auto=webp&s=8ad7e61ed048258c19aa63bf7c94d12da5b721fa submitted by /u/purton_i [link] [comments]  ( 9 min )

My colleague Piotr and I have been testing GPT-4V(ision) over the last day. We wrote up our findings, covering how GPT-4V performs on: Visual question answering (VQA) across a range of domains (locations, movies, plants) OCR Math OCR Object detection And more TL;DR: GPT-4V performed well for VQA and document OCR but struggled with OCR on real-world images and object detection (where we asked for bounding boxes). https://blog.roboflow.com/gpt-4-vision/ I would love to hear what other people have found working with GPT-4V. submitted by /u/zerojames_ [link] [comments]  ( 9 min )

A new paper proposes Show-1, a hybrid model that combines pixel and latent diffusion for efficient high-quality text-to-video generation. Both of these approaches have tradeoffs, so researchers at the National University of Singapore tried a hybrid approach combining both, and shared the results in a paper published yesterday. My highlights from the paper: Pixel diffusion excels at low-res video generation precisely aligned with text Latent diffusion acts as efficient upsampling expert from low to high res Chaining the two techniques inherits benefits of both Show-1 achieves strong alignment, quality, and 15x less inference memory The key is using pixel diffusion for the initial low-resolution stage. This retains alignment with text descriptions. Latent diffusion then serves as a super-resolution expert, upsampling efficiently while preserving fidelity. By blending complementary techniques, Show-1 moves past tradeoffs limiting the individual models. More details here. Paper is here (includes links to example generations). submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I am a beginner in Data Science. I have recently enrolled in the supervised machine learning algorithm by Andrew Ng in Coursera. I am now familiarized with linear regression, gradient descent. However, I faced a certain issue. In the optional lab, there was a task to calculate the value of the cost function using gradient descent for linear regression. I wrote the code in my notebook by myself and cross checked it to be correct. However, the desired output of w,b are very much different but the cost function yields a better result in my code. Another factor, I noticed that have to scale only thex variables, leaving the values of y. I have two major queries now: Is the yielding of different w,b values fine as long as the cost function is minimum? (w is a numpy array) Why do scale the x variables only? Why don't scale the y variables? Thanks in advance. submitted by /u/healing_you [link] [comments]  ( 9 min )

Hey r/MachineLearning, we made a tutorial that showcases typical workflows and tooling for voice analytics applications. The tutorial is intended for intermediate-level ML practitioners. The walkthrough is purely based on open source software and covers: Efficient interactive data exploration and inspection Dataset handling and inference on pre-trained models Model debugging and identification of critical data clusters Model comparison and selection ​ https://i.redd.it/j15gk3kkgyqb1.gif 🔗 Blog with code: https://medium.com/p/dbfd923a5a79#432e-3559ae606f80 🤗 Interactive demo: https://huggingface.co/spaces/renumics/emodb-model-debugging ​ ​ submitted by /u/44sps [link] [comments]  ( 9 min )

Hi everyone. In my job I work with a lot of data for Computer Vision, and I use Label Studio for annotations. But the last time I've worked with it, I lost some of my annotations, which I need for other purposes. I have the final result as a YOLO and COCO dataset, but I cannot import the results from them to recover all I need. Can you suggest me good applications with an intuitive UI to import the COCO or YOLO dataset and work with labels? submitted by /u/thattallsoldier [link] [comments]  ( 9 min )

Pymilo is an open-source Python package that offers an efficient, safe, and transparent method for transporting pre-trained machine-learning models. The motivation for developing this package is to eliminate the risks of binary or pickle formats. As this library is still in its early stages of development, it currently supports only a limited number of machine learning models provided by Scikit-learn. Nevertheless, it will be precious if the community utilizes this library and provides us with their feedback about improving the package's interface and prioritizing future developments. Your cooperation would be invaluable to us. In the following, I provide an example of how to utilize PyMilo. GitHub Repo: https://github.com/openscilab/pymilo Development Status: Alpha Simple Linear Mode…  ( 9 min )

Was asked something similar to the following question in an interview for a ML role and was curious how others would answer this: Say you have a dataset with one feature column and one label column (with different classes). Assume this data is too large to fit into memory and could be infinite in size (e.g data is coming in as a stream). How would you train a ML model on this data to accurately predict the label? Followup: instead of one feature column, what if you had several thousand? How would you decide which features to use given the size of the dataset? I discussed online sampling (resevoir sampling, etc) as a way to get a training dataset that could fit in memory + continually train on that but the interviewer did not seem convinced. Any thoughts? submitted by /u/scpdstudent [link] [comments]  ( 9 min )

I'm studying the application of ML to improve searches. Here's a couple of example scenarios: Document retrieval (search) system: We have a (source) document with us and we're trying to find a matching document in a database. The source document has text and image attributes - for simplicity let's say a title and a single image. Each search result will also be a document - with a title and at most one image. A search engine: We have a query comprised of both text and an image (like google image search allows text to be added to the query as well). Each search result will be a website with text and image attributes (for simplicity, webpage title and at most one image) More generally, I have a search system - whatever we're trying to search for has text and an image associated with it…  ( 11 min )

How does the development process of a Machine Learning project unfold phase-by-phase within your organization? Could you please specify the type of organization you are, the time spent on each phase, as well as any aspects you consider to be weak or fundamental? It would also be great if you could share any tips or tricks you've learned that have changed your perspective. submitted by /u/Spiritual_Narwhal649 [link] [comments]  ( 9 min )

Hello everyone, I am trying to detect the 9 squares of a face of a Rubik’s Cube through a camera. The idea is that I want to use my computer camera and tell the user to show all the Rubik’s Cube faces and read the faces so I can feed it to a solver. Here are the steps I have tried so far: Sharpened square edges Obtained binary image and removed noise Detected and extracted squares Some methods I used were using different blurs and cv functions but nothing worked. Sometimes, it can get all 9 squares but sometimes it doesn't. There also seems to be a difference for different colors; for example; the model can detect green squares easier than yellow squares. Can anyone provide advice as to how I can detect the squares on the face? ​ https://preview.redd.it/1ht9f4h31wqb1.png?width=2180&format=png&auto=webp&s=32d23515a43406c0f8828e6790ad71e754b0ab80 submitted by /u/uglyboi34 [link] [comments]  ( 9 min )

Posted by Zhengqi Li and Noah Snavely, Research Scientists, Google Research A mobile phone’s camera is a powerful tool for capturing everyday moments. However, capturing a dynamic scene using a single camera is fundamentally limited. For instance, if we wanted to adjust the camera motion or timing of a recorded video (e.g., to freeze time while sweeping the camera around to highlight a dramatic moment), we would typically need an expensive Hollywood setup with a synchronized camera rig. Would it be possible to achieve similar effects solely from a video captured using a mobile phone’s camera, without a Hollywood budget? In “DynIBaR: Neural Dynamic Image-Based Rendering”, a best paper honorable mention at CVPR 2023, we describe a new method that generates photorealistic free-viewp…  ( 92 min )

Ramnath Kumar, Pre-Doctoral Researcher, and Arun Sai Suggala, Research Scientist, Google Research Deep neural networks (DNNs) have become essential for solving a wide range of tasks, from standard supervised learning (image classification using ViT) to meta-learning. The most commonly-used paradigm for learning DNNs is empirical risk minimization (ERM), which aims to identify a network that minimizes the average loss on training data points. Several algorithms, including stochastic gradient descent (SGD), Adam, and Adagrad, have been proposed for solving ERM. However, a drawback of ERM is that it weights all the samples equally, often ignoring the rare and more difficult samples, and focusing on the easier and abundant samples. This leads to suboptimal performance on unseen data, espe…  ( 92 min )

This post is co-written with Ilan Geller and Shuyu Yang from Accenture. Enterprises today face major challenges when it comes to using their information and knowledge bases for both internal and external business operations. With constantly evolving operations, processes, policies, and compliance requirements, it can be extremely difficult for employees and customers to stay up […]  ( 8 min )

We’re excited to announce that Amazon SageMaker Canvas now offers a quicker and more user-friendly way to create machine learning models for time-series forecasting. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate machine learning (ML) models without requiring any machine learning experience or having to write a single line of code. SageMaker […]  ( 7 min )

In the world of data-driven decision-making, time series forecasting is key in enabling businesses to use historical data patterns to anticipate future outcomes. Whether you are working in asset risk management, trading, weather prediction, energy demand forecasting, vital sign monitoring, or traffic analysis, the ability to forecast accurately is crucial for success. In these applications, […]  ( 10 min )

In the rapidly evolving world of AI and machine learning (ML), foundation models (FMs) have shown tremendous potential for driving innovation and unlocking new use cases. However, as organizations increasingly harness the power of FMs, concerns surrounding data privacy, security, added cost, and compliance have become paramount. Regulated and compliance-oriented industries, such as financial services, […]  ( 13 min )

Companies use time series forecasting to make core planning decisions that help them navigate through uncertain futures. This post is meant to address supply chain stakeholders, who share a common need of determining how many finished goods are needed over a mixed variety of planning time horizons. In addition to planning how many units of […]  ( 11 min )

From startups to enterprises, organizations of all sizes are getting started with generative AI. They want to capitalize on generative AI and translate the momentum from betas, prototypes, and demos into real-world productivity gains and innovations. But what do organizations need to bring generative AI into the enterprise and make it real? When we talk […]  ( 13 min )

Big data has been around for some time now, becoming a more or less common concept in business. However, recent developments in AI technology have shaken up an already volatile field, inviting us to reconsider our projections of how the big data market will look in the future. We can already see the signs that… Read More »How will the Big Data market evolve in the future? The post How will the Big Data market evolve in the future? appeared first on Data Science Central.  ( 22 min )

Pixellot is scoring with vision AI — making it easier for organizations to deliver real-time sports broadcasting and analytics to viewers across the globe. A member of the NVIDIA Metropolis vision AI partner ecosystem, the company based near Tel Aviv offers an AI-powered platform that automates the capturing, streaming and analysis of sporting events. It’s Read article >  ( 7 min )

The wait is over. GeForce NOW Ultimate members can experience Cyberpunk 2077: Phantom Liberty on GOG.com at full GeForce RTX 4080 quality, with support for NVIDIA DLSS 3.5 technology. It’s part of an action-packed GFN Thursday, with 26 more games joining the cloud gaming platform’s library, including Quake II from id Software. A New Look Read article >  ( 8 min )

Powerful large-scale AI models like GPT-4 are showing dramatic improvements in reasoning, problem-solving, and language capabilities. This marks a phase change for artificial intelligence—and a signal of accelerating progress to come.    In this Microsoft Research Podcast series, AI scientist and engineer Ashley Llorens hosts conversations with his collaborators and colleagues about what these models—and the […] The post AI Frontiers: Measuring and mitigating harms with Hanna Wallach appeared first on Microsoft Research.  ( 29 min )

Hey there! Are there guys here who possess a strong grasp of AI neural network logic? ​ I've extracted a character from an anime scene using a mask, and saved it as a PNG sequence which contains solely the anime character along with an alpha (transparent) background. ​ I'm curious about how the Flowframes neural network can recreate the background that was originally behind the character but removed by the mask. It's impossible since the PNG images don't have that background info. ​ Can anyone explain how this works? ​ Attachments: - Image #1: https://preview.redd.it/z2bypfkstvqb1.png?width=1920&format=png&auto=webp&s=c534167c5ae4129c04f9b8b2fbca3bac350a1d4a - Image #2: https://preview.redd.it/x5kkzs2ttvqb1.png?width=1920&format=png&auto=webp&s=6838d7ca5e1e4f19ba46c04750fdaea537a787f0 (Don't mind the black background in the thumbnails, it's a bug, there's actually a transparent background) ​ * Flowframes is a app that utilizes advanced AI frameworks to interpolate videos in order to increase their framerate in the most natural looking way possible. submitted by /u/drkysqrl [link] [comments]  ( 9 min )

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Hey all, I’m trying to do a regression algorithm for a dataset where I have a graph for each patient I have representing a location in their brain from MRI images. Right now, I don’t have a ton of data, so I’m looking for some way to take each graph I have and get a feature vector for it to put into a regression algorithm. So for 100 patients, I have 100 graphs, I’d like to have 100 feature vectors representing each patients graph. My issue is trying to find some algorithm that takes in the entire graph and outputs a single feature vector. I’ve been looking at some libraries but they all seem wildly scattered. I don’t want to grab a bunch of nose embeddings and do some elementary merge of them, like an average or sum, etc. Any help in pointing me to some Python libraries that can help me do this, or algorithms, or anything. Thank you so much. submitted by /u/kaleb7589 [link] [comments]  ( 9 min )

Normalization in VAE[D] Am training a variational auto encoder. First I tried with batch normalization before I send the data to the network and someone probably wisely pointed out that it's not correct. If I don't use batch norm then my training fails due to numerical instability. I then tried scaling my data before hand using standard scaler from sklewrn. And now my training works. Is this reasonable? Any other thoughts? submitted by /u/Global-Gene2392 [link] [comments]  ( 9 min )

I'm following a YouTube video to create a simple machine learning model to predict stock prices. I have to reshape my prediction data so it works with inverse_transform, but in the video he doesn't do this. If I don't reshape it I get an error, but I think when I do reshape it it messes with the data. The predicted values are all very similar. I've tried messing with epoch and batch sizes, and changing other metrics like prediction_days, but nothing has worked. This is what the prediction data looks like when plotted, and this is what it looks like when printed. Does anyone know what could be causing this? Here's my code submitted by /u/darkshadowtrail [link] [comments]  ( 9 min )

This subreddit will continue to die if it doesn't foster discussion of the latest research and reduce low-quality posts. However, making a judgement as to what is or is not low-quality is time-consuming and subjective -- not something the mods should be doing. To this end, I had the following new rule idea: If it's your first time at Fight If it's your first post in this subreddit, it needs to be a link to arxiv (Or, more generally, the number of your non-arxiv posts cannot exceed the number of your arxiv posts) All arxiv posts must be standard links to the abstract page (to catch reposts and to connect discussions of the same paper in different subreddits) An arxiv post must be a paper you've read yourself, and you should post a comment describing what you liked and DIDN'T like about it (Let the airing of grievances begin! I think this will help seed the discussion, which is really the raison d'être of this subreddit) If the post or the comment get downvoted, they do not count. What do you think? Will this help steer this subreddit in the right direction? Is this enforceable? submitted by /u/we_are_mammals [link] [comments]  ( 9 min )

Hi I am a physicist (1st year in masters) and I decided to take NN class (for cs students). I have a decent experience with python but I have never done low level coding. The class project requires a C++ implementation of NN with back propagation algorithm. I am quite confident in my learning ability, nonetheless, do you guys think it is feasible for me to code such a project in 13 weeks (I also have other subjects and cant just spend all my time on this)? Thanks submitted by /u/merimace [link] [comments]  ( 9 min )

Hey fellow Redditors, I find myself in quite a situation and could use some guidance. Recently, I introduced my professor to privateGPT and demonstrated its capabilities using a small set of college data. To my delight, he was impressed and has now tasked me with researching and developing a ChatGPT-like chatbot, but with access to our university's extensive data. Here's where I need your help: my professor wants this chatbot to be hosted on our university's systems due to privacy concerns, which means I can't use ChatGPT's API. I've been given access to Sol HPC, but I'm finding it quite confusing to get started. I'm looking for advice, tips, or any resources that can help me embark on this journey. Has anyone here worked on a similar project, or does anyone have experience with Sol HPC or building chatbots with local data sources? Any guidance or insights would be greatly appreciated! Thank you in advance for your help. This project means a lot to me, and I want to make sure I'm heading in the right direction. submitted by /u/ssshankyyy [link] [comments]  ( 9 min )

Generating coherent videos spanning multiple scenes from text descriptions poses unique challenges for AI. While recent progress enables creating short clips, smoothly transitioning across diverse events and maintaining continuity remains difficult. A new paper from UNC Chapel Hill proposes VIDEODIRECTORGPT, a two-stage framework attempting to address multi-scene video generation: Here are my highlights from the paper: Two-stage approach: first a language model generates detailed "video plan", then a video generation module renders scenes based on the plan Video plan contains multi-scene descriptions, entities/layouts, backgrounds, consistency groupings - guides downstream video generation Video generation module called Layout2Vid trained on images, adds spatial layout control and cross-scene consistency to existing text-to-video model Experiments show improved object layout/control in single-scene videos vs baselines Multi-scene videos display higher object consistency across scenes compared to baselines Competitive open-domain video generation performance maintained The key innovation seems to be using a large language model to plot detailed video plans to guide overall video generation. And the video generator Layout2Vid adds better spatial and temporal control through some clever tweaks. The separation of these tasks seems to matter. You can read my full summary here. There's a link to the repo there too. Paper link is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

survival analysis in matlab hi everyone one i'm doing a predictive algorithm to find DFS using Cox regression, i first used LASSO regression to select the predictive variables, now i'm using the c-index to evaluate the predictive accuracy, and it's always equals to 1 and I can't understand why(I tried to reduce the numbers of variables just to see if it could change but it didn't change).Also, i'm working on censored date of course. can someone help me understand what I'm doing wrong? submitted by /u/bl4s3159 [link] [comments]  ( 9 min )

My annotations are in pascal voc format. Below is a calculator I am testing . Not sure if I am calculating the scale and aspect ratios correctly. Please help. import os import xml.etree.ElementTree as ET import numpy as np from sklearn.cluster import KMeans def compute_scales_and_aspect_ratios(directory, n_clusters, img_size): widths = [] heights = [] for filename in os.listdir(directory): if not filename.endswith('.xml'): continue fullname = os.path.join(directory, filename) tree = ET.parse(fullname) root = tree.getroot() for obj in root.iter('object'): xmlbox = obj.find('bndbox') w = float(xmlbox.find('xmax').text) - float(xmlbox.find('xmin').text) h = float(xmlbox.find('ymax').text) - float(xmlbox.find('ymin').text) widths.append(w) heights.append(h) widths = np.array(widths) / img_size[1] # Normalize by image width heights = np.array(heights) / img_size[0] # Normalize by image height scales = np.sqrt(widths * heights).reshape(-1, 1) aspect_ratios = (widths / heights).reshape(-1, 1) kmeans_scales = KMeans(n_clusters=n_clusters, random_state=0).fit(scales) kmeans_aspect_ratios = KMeans(n_clusters=n_clusters, random_state=0).fit(aspect_ratios) return kmeans_scales.cluster_centers_, kmeans_aspect_ratios.cluster_centers_ directory = "path_to_top_folder/batch-1" n_clusters = 5 img_size = (640, 1024) scales, aspect_ratios = compute_scales_and_aspect_ratios(directory, n_clusters, img_size) print('Scales:', scales.flatten()) print('Aspect Ratios:', aspect_ratios.flatten()) ​ submitted by /u/dpadhy [link] [comments]  ( 9 min )

I am looking for training data consisting of children’s stories and associated grade level. Does anyone know of any publicly available or paid datasets like this? submitted by /u/SpellboundLRN [link] [comments]  ( 9 min )

submitted by /u/cegras [link] [comments]  ( 9 min )

Hello! I'm working on a Tetris AI and am representing the 10x20 grid cubes with a one hot encoded dataset: https://www.kaggle.com/datasets/conlan/tetris-training-set-9262023 This means my data has 208 features (200 for the grid cubes being on/off, 7 for the "next shape" box, and 1 for the labeled best move. I currently have 9460 labeled samples and have done some preliminary fitting here: https://www.kaggle.com/code/conlan/tetris-ai?scriptVersionId=144388350 with a highest f1_macro score of 0.431090. Does anyone have suggestions for which direction to take from here to improve? Currently I see: Collect More Data Tune Hyperparameters Rework Features I'm hesitant to rework the features as that would require telling the model more specifics and would like to keep it abstract, but maybe 200 is crazy high? Or maybe <10k samples is too low and I should just keep collecting data? Thanks in advance! submitted by /u/conlanrios [link] [comments]  ( 9 min )

Paper - https://arxiv.org/abs/2304.13734 submitted by /u/MysteryInc152 [link] [comments]  ( 9 min )

ood morning everyone, I am currently reading the book of Mr. Burkov: Machine Learning Engineering. He talk about a step that might be helpful before training a ML model: Feature Scaling. Furthermore, he adds that before Feature Scaling, you might do Feature Transformation (Log, Square,...) in order to make your data look normal and have better models. How true do you think this statement is? Do you also transform your feature, and then scale them? How often do you do it? It is important for Regression or SVM, but do you do it also for other black box algorithms such as Random Forests? What are the best practices according to you? submitted by /u/dekozr [link] [comments]  ( 9 min )

So no discussions are going on about AAAI 2024, or have I just been unable to find any? Opening this regarding Phase 1-2 and Results discussions if anyone wants to discuss. If there already is a thread, share! For an opening question, any idea about what percentages are rejected in desk rejection, phase 1 and finally phase 2? (Roughly of course) submitted by /u/atharvandogra [link] [comments]  ( 9 min )

so if u go check the source code for gpt2 u can clearly see that the nrom happens inside the attention and mlp layers. and that the add is separate. this is in the official openai github and is relatively easy to read:https://github.com/openai/gpt-2/blob/master/src/model.py#L123-L130 (thx KingsmanVince) ​ for some reason all the online materials are saying that there is a full norm layer before the mlp instead of inside of it submitted by /u/rejectedlesbian [link] [comments]  ( 9 min )

Hi, Recently I am trying to reimplement a deep learning based object tracking in C++. However, the whole pipeline involve online training and weight update. Is it possible to do the training for ONNX model and using cuda as accelerator in C++? If yes, then how is the training speed (BP/update)compare to torchlib? I personally strongly prefer onnx, cuz it is much easier to deploy… submitted by /u/Independent_Bet1268 [link] [comments]  ( 9 min )

Mods finally commented The only time that mods were active is when they removed the cat meme. It has been a month since that. Let see what mods have done to improve this sub. Here are some of the other posts obviously rule-breaking or off-topic that mods do NOT remove: A person asking for help with their motherboard A person asking about statistics A person asking for machine learning roadmap Another asking-for-roadmap post ... the list goes on with absolute beginner questions, and low-quality posts. All these posts were written in less than 1 week. As we can see, mods do nothing. They only remove posts that calling out them. Here are posts that people discuss the status of this sub: 17 Sep 2023 2 Sep 2023 1 Aug 2023 Questions for mods: where are you when people complain? Why do you only show up when someone call you out? We have few options: Report the mods and the sub for unmoderated (see this 1 and this 2) Find other communities Gatekeep harder, tell people to go to r/learnmachinelearning r/MLQuestions r/cscareerquestions r/languagetechnology submitted by /u/March8365 [link] [comments]  ( 9 min )

EDIT: They released the model weights on HF (https://huggingface.co/mistralai) under a Apache 2.0 License. They also updated their website with documentation on how to use/run : https://docs.mistral.ai Note: I am not affiliated with Mistral AI. ​ Via their Twitter X account : magnet:?xt=urn:btih:208b101a0f51514ecf285885a8b0f6fb1a1e4d7d&dn=mistral-7B-v0.1&tr=udp%3A%2F%http://2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=https%3A%2F%http://2Ftracker1.520.jp%3A443%2Fannounce ​ https://preview.redd.it/0o46ls925rqb1.png?width=1306&format=png&auto=webp&s=7ff7ca3a510577e9ecdaa3c9ccb7ef763acc0780 submitted by /u/Fluid-Age-9266 [link] [comments]  ( 9 min )

I am wondering if any one runs into this before, i have finetuned flan-t5-xl for classification task by generating one token from decoder. The finetune process looks OK. I want to convert this into t5 encoder with a head to save memory. I am using huggingface T5ForSequenceClassification. However i am seeing loss not actually decrease but bounce around certain float value. What could be wrong? I have tried a few learning rates and other hyperparameters tuning. submitted by /u/Chen806 [link] [comments]  ( 9 min )

Researchers from Microsoft have just proposed using a psychological assessment tool called the Defining Issues Test (DIT) to evaluate the moral reasoning capabilities of large language models (LLMs) like GPT-3, ChatGPT, etc. The DIT presents moral dilemmas and has subjects rate and rank the importance of various ethical considerations related to the dilemma. It allows quantifying the sophistication of moral thinking through a P-score. In this new paper, the researchers tested prominent LLMs with adapted DIT prompts containing AI-relevant moral scenarios. Key findings: Large models like GPT-3 failed to comprehend prompts and scored near random baseline in moral reasoning. ChatGPT, Text-davinci-003 and GPT-4 showed coherent moral reasoning with above-random P-scores. Surprisingly, the smaller 70B LlamaChat model outscored larger models in its P-score, demonstrating advanced ethics understanding is possible without massive parameters. The models operated mostly at intermediate conventional levels as per Kohlberg's moral development theory. No model exhibited highly mature moral reasoning. I think this is an interesting framework to evaluate and improve LLMs' moral intelligence before deploying them into sensitive real-world environments - to the extent that a model can be said to possess moral intelligence (or, seem to possess it?). Here's a link to my full summary with a lot more background on Kohlberg's model (had to read up on it since I didn't study psych). Full paper is here submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Any good AI (low-hype) newsletters/blogs? That's ideally sent <= 4 times a month? I'm tired of the amount of AI news I have to go through daily just to keep up. submitted by /u/onteri [link] [comments]  ( 9 min )

AI-generated deepfake clones of Chinese livestream influencers are becoming popular on e-commerce platforms. These clones can work 24/7 and help brands sell their products without the need for human streamers. Chinese startups and tech companies are offering the service of creating these deepfake avatars for a cost of around $1,000. The technology has evolved over the years, with the need for training videos decreasing from 30 minutes to just one minute. The AI clones can mimic the movements and speech of human streamers, making them an affordable and efficient alternative for smaller brands. Source : https://www.technologyreview.com/2023/09/19/1079832/chinese-ecommerce-deepfakes-livestream-influencers-ai/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

Automatic code generation has always been an integral part of programming: compilers, synthesis tools, convertors, etc. are examples of classic code generators. Now, with such powerful LLMs at hand, it is only natural to try to find new ways to generate codes. The question is: are LLMs the right tool for code generation? There are two sides to code generation: (1) understanding the intent (a.k.a. capturing the spec) (2) writing the code. LLMs are great for (1), but not so good for (2). This is an example of using LLM for general-domain code generation: https://github.com/RoboCoachTechnologies/GPT-Synthesizer You can see that the main focus here is to properly capture the spec, and that's where LLMs shine. LLMs solution for a general-domain code generation may not be complete or optimized. It is always easier to break the problem and solve code generation in a specific domain. Here you can see how much better and cleaner the output of code generation can be when it is limited to a specific domain (robotics domain, ROS in particular, in this case): https://github.com/RoboCoachTechnologies/ROScribe What are your thoughts on using LLMs for code generation? submitted by /u/RoboCoachTech [link] [comments]  ( 9 min )

submitted by /u/waozen [link] [comments]  ( 9 min )

The CIA is developing an AI chatbot similar to ChatGPT to help US intelligence agencies sift through large amounts of information. The program will train on publicly available data and provide sources for agents to confirm their validity. The tool will allow agents to look up information, ask follow-up questions, and summarize daunting masses of data. The exact nature of what constitutes 'public data' could spark privacy issues. The tool will be distributed to the 18-agency US intelligence community, but not to lawmakers or the public. Source : https://www.engadget.com/even-the-cia-is-developing-an-ai-chatbot-192358767.html submitted by /u/NuseAI [link] [comments]  ( 9 min )

Generating coherent videos spanning multiple scenes from text descriptions poses unique challenges for AI. While recent progress enables creating short clips, smoothly transitioning across diverse events and maintaining continuity remains difficult. A new paper from UNC Chapel Hill proposes VIDEODIRECTORGPT, a two-stage framework attempting to address multi-scene video generation: Here are my highlights from the paper: Two-stage approach: first a language model generates detailed "video plan", then a video generation module renders scenes based on the plan Video plan contains multi-scene descriptions, entities/layouts, backgrounds, consistency groupings - guides downstream video generation Video generation module called Layout2Vid trained on images, adds spatial layout control and cross-scene consistency to existing text-to-video model Experiments show improved object layout/control in single-scene videos vs baselines Multi-scene videos display higher object consistency across scenes compared to baselines Competitive open-domain video generation performance maintained The key innovation seems to be using a large language model to plot detailed video plans to guide overall video generation. And the video generator Layout2Vid adds better spatial and temporal control through some clever tweaks. The separation of these tasks seems to matter. You can read my full summary here. There's a link to the repo there too. Paper link is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I created this cyberpunk inspired short using Midjourney to create the pictures, RunwayML to animate them, and then edit them together using CapCut on iOS. I know the animation is still in early stages, but what do you think? Do you think we could have full length movies in a couple years? submitted by /u/Exitium_Maximus [link] [comments]  ( 9 min )

How do you evaluate the quality of your prompt outputs? Here's a handy checklist. Let's have a look! You can also join r/PromptWizards to find more tutorials and prompts! Part 1: Understanding AI's Understanding You've presented a prompt to your AI, the next questions are: Has the AI accurately grasped the context? If not, how can I make sure the LLM steers my context better, should I be more direct and clear in my prompt? Can I be less negative (shows to perform less) and be more guiding to the LLM? Do the responses directly address the question or topic? Was my query and task/instruction clearly detailed in enough depth that the LLM understood what I expect? Are there any contradictions between different responses to the same prompt? If I run my prompt multiple times, i…  ( 10 min )

OpenAI's much-touted model GPT-4, lauded for its multimodal abilities, including advanced image recognition, still has significant flaws. These glitches range from inventing facts to misinterpreting chemicals' images and hate symbols, according to a new paper from OpenAI. To stay ahead of AI developments, look here first. https://preview.redd.it/seg5x4zn3uqb1.png?width=1108&format=png&auto=webp&s=635a6c58cf6255f62d8eae3077678864e5b0e248 Unintended GPT-4V behaviors GPT-4V has a tendency to hallucinate or invent facts with unwarranted confidence. The model struggles to make correct inferences, sometimes creating fictional terms by wrongly combining text strings. It misinterprets certain symbols of hate and can give incorrect answers in the context of medical imaging. OpenAI’s mitigation strategies OpenAI has implemented various safeguards to prevent GPT-4V's misuse, such as breaking CAPTCHAs or using images to infer personal details. The company insisted that GPT-4V is not to be used for identifying dangerous chemicals from image structures. OpenAI acknowledged it has a long way to go in refining the model and is working on it. Discrimination and bias When OpenAI’s production safeguards are disabled, GPT-4V displays bias against certain sexes and body types. The paper reported offensive responses related to body positivity when prompted by an image of a woman in a bathing suit. (source) P.S. If you like this kind of analysis, I write a free newsletter that dissects the most impactful AI news and research. 1000s of professionals from Google, Meta, and OpenAI read it daily. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

submitted by /u/degrudv [link] [comments]  ( 9 min )

In using Ai and asking it to do simple tasks like "explain this in more simple terms" or asking it to make flashcards for me in a certain format, I am really convinced that language models, (bard and openai specifically) are being nerfed. They cannot understand simple instructions as well anymore. I had a paragraph of information for one of my classes that I wanted it to make more straightforward for me before I actually went to class the next day. I spent like 30 minutes trying to get it to do that and eventually just ended up giving up. Why dont language models feel as sharp as they did say a year ago? I wish I had more examples to share. Am I the only one who's noticed this? submitted by /u/Bojof12 [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 9 min )

Hi all – I’m new to this sub-Reddit, so hopefully I’m in the right place. I am working on an audio production that will span multiple episodes and hopefully multiple seasons. It will require many characters, ranging in gender, age, ethnicity, etc. I am a decent voice actor and can do many of the roles myself, but some of them I cannot fake using my voice alone. My budget is very limited, so I was hoping to find some type of software I can change my voice for the production. This can be during the recording process, or after recording… As long as it gets the job done, and makes me sound like someone else entirely. Does anybody know of a software that can achieve this? Most of the software I found either specifically designed to change users voice on the spot and is aimed at gamers changing your voice for live streams or in-game chats. I’m also on a Mac, which I know will be limiting. I’m having a hard time finding something I can use. Any suggestions will be helpful. Thank you! EDIT: To clarify, I don’t want to just change my voice to sound different in general. I want to specifically sound like a woman, an elder man, or someone of a different ethnic background. Those are just a few examples. submitted by /u/nopetoocreepy [link] [comments]  ( 9 min )

submitted by /u/GABIBBOPAZZOCINESE [link] [comments]  ( 9 min )

submitted by /u/rattuspuer [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 9 min )

Predicting alternative historical scenarios is always fraught with complexity and uncertainty, especially concerning highly politically charged topics. However, let's entertain the hypothetical situation where the insurrection following the 2020 U.S. presidential election had succeeded, with the understanding that this is speculative reasoning. In this alternate reality, the immediate consequences would be a constitutional crisis of unprecedented proportions. Faith in democratic institutions would be severely eroded, both domestically and internationally. Trump's retaining power in this manner would spark widespread protests, perhaps more intense and larger than those seen in the summer of 2020. The unrest would likely lead to a governmental response that could be more authoritarian, poss…  ( 10 min )

submitted by /u/ApprehensiveChair460 [link] [comments]  ( 9 min )

Hey, so I'm hoping to get a bit of help with finding an art generator to play around with, my only concern is the giving my entire personal information away, are there any apps for Android whereby the tos aren't crazy invasive by any chance? submitted by /u/Fluffy_Discount_9692 [link] [comments]  ( 9 min )

The European Union is urging the implementation of more safeguards against the risks posed by generative AI tools to free and fair debate in democratic societies, especially during elections. The EU's values and transparency commissioner has highlighted the potential threat of AI-generated disinformation to elections and called for platforms to be vigilant and provide efficient safeguards. Mainstream platforms have made initial efforts to address the risks by implementing safeguards to inform users about the synthetic origin of content posted online. The EU commissioner is meeting with representatives from OpenAI to discuss the issue. An incoming pan-EU AI regulation, the EU AI Act, is expected to make user disclosures a legal requirement for generative AI technologies. The EU's voluntary anti-disinformation Code has 44 signatories, including major social media and search platforms, as well as entities from the ad industry and civil society organizations. Google, one of the signatories, has published a report discussing the social impacts of AI and its commitment to developing technology responsibly. Google Search has published guidance on AI-generated content and plans to integrate new innovations in watermarking, metadata, and other techniques into its generative models. The EU's Code of Practice on Disinformation is seen as a stop-gap measure until the EU AI Act is adopted and mandatory deepfake disclosures are enforced. Source : https://techcrunch.com/2023/09/26/generative-ai-disinformation-risks/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

Chinese media reported that BIDU’s Baidu AI Cloud has released ACE 3.0, an intelligent traffic solution comprehensively restructured using a foundation model. ACE means Autonomous Driving, Connected Road, and Efficient Mobility respectively.[1] BCG consultants solving business problems with OpenAI’s GPT-4 performed 23% worse than those without it, new study finds.[2] CIA Builds Its Own Artificial Intelligence Tool in Rivalry With China.[3] Facebook parent is developing bots with personalities, including a ‘sassmaster general’ robot that answers questions.[4] Sources: [1] http://www.aastocks.com/en/stocks/news/aafn-con/NOW.1296238/popular-news/AAFN [2] https://finance.yahoo.com/news/bcg-consultants-solving-business-problems-081532840.html [3] https://www.bloomberg.com/news/articles/2023-09-26/cia-builds-its-own-artificial-intelligence-tool-in-rivalry-with-china#xj4y7vzkg [4] https://www.wsj.com/tech/ai/meta-ai-chatbot-younger-users-dab6cb32 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I was wonderhow someone made the destroyed building look like a cat. Anyone know how to do this? submitted by /u/Agitated-Court-2871 [link] [comments]  ( 9 min )

As title, I’ll be getting my hands on a couple of decent GPUs, including an old A6000, and am excited for everything its 48GB of VRAM unlocks. What’s something interesting I should do with it? A few things off the top of my head: See what crazy things stable diffusion generates at an insane resolution (how high of a resolution would 48GB allow?) Train good Dreambooth models (or what newer methods are there for style and object training?) Run and compare various open-source LLMs (should be able to run 70b models? Generate something of decent length with MusicGen Gaussian Splatting Distribute voice recognition, TTS, audio2face, LLM, and rendering across 2 or 3 machines to create a realistic virtual human (suggestions for excellent TTS would be appreciated) What other interesting models are out there to experiment with? submitted by /u/DsDman [link] [comments]  ( 9 min )

Researchers from Microsoft have just proposed using a psychological assessment tool called the Defining Issues Test (DIT) to evaluate the moral reasoning capabilities of large language models (LLMs) like GPT-3, ChatGPT, etc. The DIT presents moral dilemmas and has subjects rate and rank the importance of various ethical considerations related to the dilemma. It allows quantifying the sophistication of moral thinking through a P-score. In this new paper, the researchers tested prominent LLMs with adapted DIT prompts containing AI-relevant moral scenarios. Key findings: Large models like GPT-3 failed to comprehend prompts and scored near random baseline in moral reasoning. ChatGPT, Text-davinci-003 and GPT-4 showed coherent moral reasoning with above-random P-scores. Surprisingly, the smaller 70B LlamaChat model outscored larger models in its P-score, demonstrating advanced ethics understanding is possible without massive parameters. The models operated mostly at intermediate conventional levels as per Kohlberg's moral development theory. No model exhibited highly mature moral reasoning. I think this is an interesting framework to evaluate and improve LLMs' moral intelligence before deploying them into sensitive real-world environments - to the extent that a model can be said to possess moral intelligence (or, seem to possess it?). Here's a link to my full summary with a lot more background on Kohlberg's model (had to read up on it since I didn't study psych). Full paper is here submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I would like to embark on online training for an F1TENTH racing car, starting from scratch and leveraging demonstration data. Currently, it appears that DDPGfD is a promising approach. Does anyone have any research papers they can recommend or suggestions on how to get started? submitted by /u/anointedninja [link] [comments]  ( 9 min )

submitted by /u/gwern [link] [comments]  ( 9 min )

Reinforcement learning has grabbed my interest pretty firmly and been my focus for the 3 months or so. I spend most of my time working in python , rust, and now mojo. Not an expert yet but my coding skills are improving. I have no degree and have taught myself most of what I know. That part is why Im looking for advice from you all. Practically every job post Ive seen has college requirements. Is it unlikely to get hired without a degree? Additional information: I'm currently working on projects for github but those arent quite done. My main interest is related to RL in game design. Applications of distributional RL in action dense environments and VR. Currently using Godot engine the most and have used pytorch, openai gym, and tensorflow (to a lesser degree). The abstract concepts of neural networks comes easy to me and Ive been following basic neurology as well. submitted by /u/SchrodingersCog [link] [comments]  ( 9 min )

Edit: I may be jumping the gun here but I think I figured it out (looks good so far). I give the episode reward for every action EXCEPT the "end early" action, now I will need to give some boost for shorter episodes to achieve the desired effect :) I feel like I'm experiencing déjà vu, posing another DQN-related question. But, here's my issue: I've set up an environment where an agent can interact for 40 steps or choose to end the interaction early with a specific action. The catch is that the reward is only given at the end of the episode, which seems to be leading the agent to strongly favor the "end early" action. Despite all other steps getting a reward of 0, I assumed the long-term reward estimate, V(s_{t+1}), would mitigate this, but the agent still heavily gravitates towards ending the episode early. Attempted Solutions: Distributed the end-of-episode reward across all prior actions taken by the agent. Considering: Replacing the "end early" action with a "do nothing" action, allowing the episode to always play out in full. However, this seems like it could introduce additional computational costs and noise. Has anyone encountered a similar problem? I'd appreciate any advice or recommendations. submitted by /u/Vae94 [link] [comments]  ( 9 min )

I learned about DeepNash and R-NaD yesterday. I read the Deepmind article, Science paper, and the source code of rnad.py. But I don't think I understand it! Part of this is that they didn't define all the terms and Greek they use in the paper, and part of it is that I don't have academic ML experience. Below is my attempt to summarize the paper in non-academic terms. I'm trying to show that I did my homework, and also I'm trying to invoke Godwin's Law in the hopes that someone will come along and correct me. Here goes: Naïve reinforcement learning doesn't work with simultaneous choice games such as matching pennies or Rock-Paper-Scissors. In naïve RL, If I choose Rock as my move, my opponent chooses Scissors as their move, and I see that I won, that will reinforce a belief that Rock is a "good" move and Scissors is a "bad" move. But this isn't true! This means that, during selfplay, a naïve RL agent will just cycle through strategies, as the timestep-(τ_n) agent learns how to beat the timestep-(τ_n-1) agent. The agent will never learn that RPS is a game about staying unpredictable! R-NaD fixes this by adjusting the reward function. I think "regularizing" is ML-academic speak for "adjusting". It adjusts the reward function in such a way that the agent will converge at a Nash equilibrium strategy. The paper's equation (1) describes how the regularization works. They didn't explain all the terms, though. I still don't know what a_i represents. But I think it corresponds to parts of the code like this line and this line. The key is that we're merging policies from multiple epochs and making sure that the current agent's move probabilities fare well against not only itself, but also against its previous two generations. They've proven that three generations is all you need to eventually converge to a Nash equilibrium. So... that's my understanding. Does anyone with actual ML experience want to weigh in? submitted by /u/lord_braleigh [link] [comments]  ( 10 min )

The iconic sci-fi opera “VALIS,” first composed by Professor Tod Machover in 1987, reboots at MIT for a new generation.  ( 11 min )

Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.  ( 10 min )

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon Warehouses across Europe and the MENA region. The design and deployment processes of projects involve many types of Requests for Information (RFIs) about engineering requirements regarding Amazon and project-specific guidelines. These requests range from simple retrieval of baseline […]  ( 13 min )

MDaudit provides a cloud-based billing compliance and revenue integrity software as a service (SaaS) platform to more than 70,000 healthcare providers and 1,500 healthcare facilities, ensuring healthcare customers maintain regulatory compliance and retain revenue. Working with the top 60+ US healthcare networks, MDaudit needs to be able to scale its artificial intelligence (AI) capabilities to […]  ( 5 min )

DENZA, the luxury electric-vehicle brand and joint venture between BYD and Mercedes-Benz, is debuting new intelligent driving features for its entire N7 model lineup, powered by the NVIDIA DRIVE Orin system-on-a-chip (SoC). The N7 series was introduced earlier this year as a family of spacious five-seater SUVs for commuters looking to sport a deluxe EV Read article >  ( 5 min )

Medical-device company Invenio Imaging is developing technology that enables surgeons to evaluate tissue biopsies in the operating room, immediately after samples are collected — providing in just three minutes AI-accelerated insights that would otherwise take weeks to obtain from a pathology lab. In a surgical biopsy, a medical professional removes samples of cells or tissue Read article >  ( 6 min )

As generative AI sweeps across corporate boardrooms around the world, global telecommunications companies are exploring how to cost-effectively deliver many of these new AI applications to the edge over 5G and upcoming 6G networks. Telcos plan to deploy over 17 million 5G microcells and towers worldwide by 2025. Building, managing and optimizing this new infrastructure Read article >  ( 6 min )

Chunked prefills & decode-maximal batching boost LLM inference; DragNUWA combines text, image, and trajectory for fine-grained video content control; reconstructing images from human brain signals; structural inequalities in creator-audience relationships. The post Research Focus: Week of September 25, 2023 appeared first on Microsoft Research.  ( 9 min )

About three years ago I ran across a strange coordinate system in which familiar functions lead to interesting plots. The system is called “circular coordinates” but it is not polar coordinates. This morning I was playing around with this again. Here’s a plot of f(x) = x. And here’s a plot of f(x) = cos(8x). […] Circular coordinate art first appeared on John D. Cook.  ( 5 min )

I found the paper “ChatGPT-steered Editing Instructor for Customization of Abstractive Summarization” published in march and I was looking for information about the cost of training such a system. Have someone tried ? Is there some weights in the nature already trained for the instructor model ? I have found the GitHub associated with the paper but it obviously only contain the code for training but no information about approximate token used or anything like that. submitted by /u/Agreeable-Committee6 [link] [comments]  ( 9 min )

We noticed a lot of people wanting to deploy computer vision models, so we built an interactive demo of OWL-ViT to show how it might be used by an end user when integrated into a product. OWL-ViT is a new object detection model from the team at Google Research. It allows you to identify an object in one image (the “query image”) and then find that same object in any number of target images. Here is the link to interact with an OWL-ViT model! submitted by /u/modelbit [link] [comments]  ( 9 min )

hey everyone, novice at ML and trying to do a project on my own. I am trying to predict the rainfall amount in inches for a given day. I’ve decided to make it a classification problem and predict the zone of rainfall as in 0-0.5 in inches or 1-1.5 inches. My data set has ~40,000 samples however i have noticed that 24,000 of them have 0.0 as the amount of rainfall. And a high percentage of the rest are very low like below 0.5 inch. I’m wondering if there’s still a way to create the type of model I had originally intended or not. Is there a way to reduce the size of my data set , specifically the amount of low values without losing important feature information? Thank you and any help is appreciated :) submitted by /u/RepeatResponsible499 [link] [comments]  ( 9 min )

Hi all, I understand it all comes down to personal preference and that it is an old topic, but a bit advice would be welcome. My current workload consists of analyzing large medical records, medical images (upcoming work) with mainly PyTorch. Now I have direct and remote access to my personal lab pc which has configuration: core i9-9900K, 32 GB Ram, GTX 2080Ti 12 GB, Windows 11. Now I am planning to buy a laptop that would help with coursework, research paper reading and remote access to my lab PC. It should last at least 4/5 years (My current 5-year-old MSI laptop's hinge broke). I have the following laptops in mind with a budget of around $2000 14-inch Macbook Pro with 16 GB RAM and M2 PRO = $1999 ASUS ROG Zephyrus 15.6" WQHD 165Hz Gaming Laptop, AMD Ryzen 9 6900HS,16GB DDR5 4800Mhz RAM, 1TB SSD PCIe 4.0 Storage, NVIDIA GeForce RTX 3060 = $1400 submitted by /u/Furiousguy79 [link] [comments]  ( 9 min )

I've seeing some people saying thar python is for training models and rust is for deploying them. Is it a widespread practice or it's just a localized need for companie with "performance sensitive" models? submitted by /u/horace_desplein [link] [comments]  ( 9 min )

Happy to share Vectara's new state-of-the-art embedding model, called Boomerang. Embedding models were so far not too much in the spotlight relative to chat models like ChatGPT, but for Retrieval-augmented-generation applications, getting the best embedding model matters a lot. would love to hear what has been the experience of others in this respect - what embedding models have worked best so far with RAG? Blog post: https://vectara.com/introducing-boomerang-vectaras-new-and-improved-retrieval-model/ Hackernews: https://news.ycombinator.com/item?id=37661359 submitted by /u/ofermend [link] [comments]  ( 9 min )

Would you deploy a self-driving car model that was trained on images for which data annotators accidentally forgot to highlight some pedestrians? Errors in object detection examples found via cleanlab. Annotators of real-world object detection datasets often make such errors and many other mistakes. To avoid training models on erroneous data and save QA teams significant time, you can now use automated algorithms invented by our scientists. Our newest paper introduces Cleanlab Object Detection: a novel algorithm to assess label quality in any object detection dataset and catch errors (named ObjectLab for short). Extensive benchmarks show Cleanlab Object Detection identifies mislabeled images with better precision/recall than other approaches. When applied to the famous COCO dataset, Cleanlab Object Detection automatically discovers hundreds of mislabeled images, including errors where annotators mistakenly: overlooked an object that should’ve had a bounding box, sloppily drew a box in a poor location, or chose the wrong class label for an annotated object. We’ve open-sourced one line of code to find errors in any object detection dataset via Cleanlab Object Detection, which can utilize any existing object detection model you’ve trained. For those interested, you can check out the 5-minute tutorial to get started and the blog to read the details. submitted by /u/jonas__m [link] [comments]  ( 9 min )

Hi there community, I hope everyone is doing well ::] I’m exploring ada-002 embedding model for building a recommendation system (along some other similarity search things like generating playlists), so naturally a lot of questions started to pop. But before goign deeper, let me explain what I am building and how the data is structured: Imagine a music app with song recommendations based on all the users history and musical metadata. Currently I have a table with a couple data on it just for tests - the users, the artists and the songs. Each of these columns have their own rows, for example song have genres, danceability, number of likes, etc. I am now implementing two more columns for history logs - a “history” (that will be related with users and songs) and a “session” (wich is a coll…  ( 12 min )

I am trying to do an Object Detection project, Does anyone know where I can find Pre-Annotated image dataset submitted by /u/Nomadic-Foe-011 [link] [comments]  ( 9 min )

I'm working on my thesis and want to perform 3D scene understanding and VQA. My scenes would be textured meshes (or pointclouds). My goal is not only to know the objects present in the scene but also the spatial relationships between them, like chair is in front of the couch, bottle is on the table etc. I want to know if there is a 3D scene encoder like the 2D image encoder ViT L/14 from CLIP. My search hasn't resulted much yet in this direction, but I have come across papers that render a 3D scene in multiple angles and then use 2D scene encoders on them. So I'd like to ask the community: Are there 3D scene encoders like CLIP ViT If not, is there's any other way that I can approach this problem. submitted by /u/Bluebird705 [link] [comments]  ( 9 min )

Hey, fellow machine learning enthusiasts! AIRI researchers are thrilled to share some exciting news with you all. Our paper, "StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation", has been accepted to ICCV 2023! 🥳 Abstract: Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g., StyleGAN) to a specific domain with few samples (e.g., painting faces, sketches, etc.). While there are many methods that tackle this problem in different ways, there are still many important questions that remain unanswered. In this paper, we provide a systematic and in-depth analysis of the domain adaptation problem of GANs, focusing on the StyleGAN model. We perform a detailed exploration of the most i…  ( 10 min )

Title pretty much says it all. It would be really cool if we have more AI tools that don't just straight up generate an image but help artists in their own art process. submitted by /u/salehxoxo [link] [comments]  ( 9 min )

When switching to a new role what did you do to ensure that you succeed? What lessons did you learn from your previous job that you took into your new job? For example Im in the process of switching jobs and one of the things I’ve learnt is that when delivering results (during fire drills) the way I write my code is focused on simply getting the results out vs being organized, efficient and scalable. While I get from point A to point B the way I get from point A to point B is not the most efficient. I think something I can do is take a step back and take a top down approach to problem solving when I enter my new role. submitted by /u/Terrible-Hamster-342 [link] [comments]  ( 9 min )

Next week will take place the International Conference on Computer Vision ICCV2023 in Paris. If you are not going, stay in touch by subscribing to the ICCV Daily magazine. It's free: https://www.rsipvision.com/feel-iccv-iccv/ Full daily previews and reports of selected ICCV papers and events. https://preview.redd.it/yxmf2ksomlqb1.jpg?width=794&format=pjpg&auto=webp&s=7063c770e7a02d0ca7bba6f41ecc36438aa86256 submitted by /u/Gletta [link] [comments]  ( 9 min )

https://preview.redd.it/4kmpzlb5clqb1.jpg?width=1140&format=pjpg&auto=webp&s=b09660dfebbd5947dc14020ca43df29b05cb82d9 In a recent development, Tencent AI Lab has launched AutoPrep, a preprocessing framework explicitly crafted for in-the-wild speech data. This innovative framework is positioned to change the landscape of speech data processing by offering automated preprocessing and high-quality annotation for unstructured speech data, addressing the longstanding challenges in the field. Read the full story submitted by /u/El-Professor-1 [link] [comments]  ( 9 min )

Training giant AI models like GPT-3 requires large resources - thousands of GPUs running for months. As a solo researcher without access to that kind of scale, I can't easily reproduce experiments and findings from papers on huge models. But a new paper from DeepMind shows you can recreate and study training instabilities seen in massive models by using small ones. The key is increasing the learning rate: This reproduces "attention collapse" where the model focuses on just a few tokens, like overfitting. Also can reproduce "logit divergence" where output values drift unstably. These issues have been reported when scaling up to billions of params. The cool part is techniques that fix them for giant models also work for small models: qk-layernorm prevents attention collapse. Adding a "z-loss" term stops logit divergence. Some other highlights from the paper include: Longer warmup helps stability, especially for bigger models. Decoupling LR and weight decay improves stability. Depth increases sensitivity much faster than width. Can predict upcoming issues from scaling trends. Default epsilon hurts at large scale. If the authors are right, one more tool that lets researchers study and even help train giant models without Google-size resources. Small models can guide large model development, sort of like how you can build a scale train set to study and improve how a railroad system works... for a lot less money than starting your own railroad company, buying land, building real tracks, etc. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

submitted by /u/JustAddMoreLayers [link] [comments]  ( 9 min )

Worked on this beautiful analysis for some time and wanted to share how nice data visualization animations can look. It makes the whole process of data science feel like a story. And AI is mere mathematics. Mathematics are beautiful, and they can be understood A paper will be published soon, detailing the algorithms and the final results https://youtu.be/P-gHTqxCo_g?si=7clD0nb16EIDprkW submitted by /u/SemperZero [link] [comments]  ( 9 min )

John Carmack, celebrated software engineer and founder of Keen Technologies, and Dr. Richard Sutton, Chief Scientific Advisor at the Alberta Machine Intelligence Institute (Amii) announce a partnership to bring greater focus and urgency to the creation of artificial general intelligence (AGI). This partnership is the first public milestone for Keen Technologies, following its initial funding announcement in August of 2022. The initial $20M round was led by Nat Friedman, Danial Gross, Patrick Collision, Tobi Lutke, Jim Keller, Sequoia Capital, and Capital Factory. In December 2022, Carmack departed as consulting CTO at Meta to focus his efforts on AGI. Sutton is the principal founder of the field of reinforcement learning. His work with Keen Technologies advances previously announced research priorities to understand basic computational intelligence. Through this work, documented in part in The Alberta Plan, Sutton seeks to understand and create long-lived computation agents that interact with a vastly more complex world and come to predict and control their sensory input signals. https://www.amii.ca/latest-from-amii/john-carmack-and-rich-sutton-agi/ submitted by /u/blabboy [link] [comments]  ( 9 min )

Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. In the latest update, I have added support for quad-precision double-double emulation for FFT calculation on most modern GPUs. I understand that modern ML is going in the opposite low-precision direction, but I still think that it may be useful to have this functionality at least for some prototyping and development of concepts. The double-double approach to the evaluation of quads represents an FP128 number as an unevaluated sum of two double numbers (like 1 and 1e-17 - the second one is smaller than 1 ULP of the first one). This increases the significand from 53 to 106 bits, allowing to do operations on numbers varying up to 32 orders of magnitude. The range of numb…  ( 10 min )

TYPO IN THE TITLE: I wanted the title to be "why should neural networks be end-to-end?" My lazy ass was swipe texting on my phone and this typo happened. FML Especially in robotics, there's been a lot of research for end-to-end neutral networks where an image is the input and the control action is the output, for say, tasks like pick an object and place it somewhere. I feel this is very restrictive while developing such a solution because it tightly couples the "control" network to the "estimation" network. This reduces modularity in building the solution, i.e., if I find a better controller architecture (machine learning based or rule based) I'm unable to use that for the task I'm solving. Moreover it seems like the generalizability of this task suffers because training this network to do this task may make it very good at only doing that specific task and the explainability of the decision making goes out of the window because it is black box. Additionally doesn't more parameters mean requiring more data to train the model? I don't see much use from such network architectures. However, I do see the applications in machine translation where you simply train the encoder and decoder to the target language at the same time with a single loss function. It seems useful there, but maybe not so much in robotics. Can someone tell me their thoughts on end-to-end architectures? Let's have a healthy discussion Edit: typo submitted by /u/piccadilly_nickadeli [link] [comments]  ( 9 min )

As the title says, what are the best podcasts to listen to that discuss new machine learning and AI advancements, new papers, and such? submitted by /u/darthJOYBOY [link] [comments]  ( 9 min )

Hi! I am working on a content based recommender system that uses bio inspired optimization algorithms. The problem is, there aren't many resources online pertaining to this concept. Initially i thought it could be used to optimize text similarity. So is it possible to do that? Or it could be used to optimize any other aspect of the system? Please let me know if you have any knowledge! Thanks! submitted by /u/Jellyfishh_13 [link] [comments]  ( 9 min )

Is this done with computer vision or somehow within the game itself? Also, what would you recommend as a resource to learning about machine learning fast? Is Microsoft Azure worth it or should I use TensorFlow? (I'll mostly do audio classification and game simulations) https://www.youtube.com/watch?v=tVNoetVLuQg Thanks so much! Please recommend go-to resources you've used to get up and running. Looking forward to what yall recommend. submitted by /u/Fit-Replacement7245 [link] [comments]  ( 9 min )

Today’s date, US style, is 9/26/2023, and there is only one group, up to isomorphism, of size 9262023. You could verify this in Mathematica with the command FiniteGroupCount[9262023] which returns 1. For a given n, when is there only one group of size n? There are two requirements. First, n has to be the product […] When there is only one group of a given size first appeared on John D. Cook.  ( 5 min )

Simple groups are the building blocks of groups similar to the way prime numbers are the building blocks of integers. This post will unpack this analogy in two ways: How do simple groups compare to prime numbers? How does the composition of simple groups compare to the composition of prime numbers? The former analogy is […] Analogy between prime numbers and simple groups first appeared on John D. Cook.  ( 6 min )

The word “normal” in mathematical nomenclature does not always means “usual” or “customary” as it does in colloquial English. Instead, it might that something has a convenient property. That is the case for normal subgroups. We can do things with normal subgroups that we cannot do with other subgroups, such as take quotients, and so […] Normal and non-normal subgroups first appeared on John D. Cook.  ( 7 min )

Hey, long story short, I've used Otter.ai for recording, and transcribing my ideas on the fly and it's really, really good! The only thing it is missing for my use case is to be able to edit the transcripts (remove some parts for example) and then have that piece be removed from the audio file as well, so you can see how long is the actual useful part that.. I also need it to have an app, since the whole point of doing this is catching ideas that just rush to my head. Apparently DeScribe has this option, but I haven't tried it and it doesn't work on mobile anyways. I know it's probably not available, but does anyone know any services similar to this? I don't need an AI bot, don't care about integration with other apps, and will not use it for meetings. TLDR: I just want an app to be able to record, and then transcribe my ideas, and then allow me to edit/fine-tune the transcript and have the audio file be edited in the same way as well.. Thanks! submitted by /u/reza2kn [link] [comments]  ( 9 min )

I have lots of lyrics I've written with the melody but I don't know how to play an instrument. submitted by /u/82brighteyes [link] [comments]  ( 9 min )

Hello all, Is it possible to use a tool or site for free that generates any random historical figure with a shirt of my choosing? Thank you all submitted by /u/JYanezez [link] [comments]  ( 9 min )

submitted by /u/stefanbg92 [link] [comments]  ( 8 min )

👋 Hey Reddit! Let's dive into the realm of Prompt Chaining. If you want to check out more prompt chain examples, then we invite you to join our community at r/PromptWizards. 🔗 Prompt Chaining: More Than Meets the Eye In the world of AI interaction, Q&A sessions with ChatGPT are thrilling. They offer fascinating glimpses into AI's creative potential and can even transform into a productive brainstorming session. But what happens when we need reliable, consistent outputs, especially for applied use cases? Enter Prompt Chaining. Prompt Chaining is a technique that breaks down complex tasks into manageable sub-steps and induces a chain reaction of responses. It allows us to use the output of one prompt as the input for the next, thereby creating a coherent, consistent, and reliable chai…  ( 10 min )

I would like to make realistic stuff using screenshots I took in video games.I know there are plenty of text to image AI tools, but are you guys familiar with image to image ones? submitted by /u/LauraLuna99 [link] [comments]  ( 9 min )

Training giant AI models like GPT-3 requires large resources - thousands of GPUs running for months. As a solo researcher without access to that kind of scale, I can't easily reproduce experiments and findings from papers on huge models. But a new paper from DeepMind shows you can recreate and study training instabilities seen in massive models by using small ones. The key is increasing the learning rate: This reproduces "attention collapse" where the model focuses on just a few tokens, like overfitting. Also can reproduce "logit divergence" where output values drift unstably. These issues have been reported when scaling up to billions of params. The cool part is techniques that fix them for giant models also work for small models: qk-layernorm prevents attention collapse. Adding a "z-loss" term stops logit divergence. Some other highlights from the paper include: Longer warmup helps stability, especially for bigger models. Decoupling LR and weight decay improves stability. Depth increases sensitivity much faster than width. Can predict upcoming issues from scaling trends. Default epsilon hurts at large scale. If the authors are right, one more tool that lets researchers study and even help train giant models without Google-size resources. Small models can guide large model development, sort of like how you can build a scale train set to study and improve how a railroad system works... for a lot less money than starting your own railroad company, buying land, building real tracks, etc. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

The idea of AI systems engaging in recursive self-improvement is a key tenet of what some call the "intelligence explosion" hypothesis. Here's how it might go down within the next few years: Baseline Capability: First, we start with a machine learning model that's good, but not necessarily groundbreaking. The important part is that it has some capacity for basic code generation and analysis. Guided Improvements: Human experts will likely initiate the process by enabling the AI to optimize specific aspects of its code. We're talking parameters, efficiency tweaks—stuff like that. It's like giving the AI a nudge in the right direction. Self-Evaluation Metrics: The AI needs to understand when it's actually getting smarter. That's achieved through predefined performance metrics which could b…  ( 10 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 9 min )

submitted by /u/fookingyeah [link] [comments]  ( 9 min )

For example there is a story line and I need music accordingly. Or in fact any bgm tools that help sound engineers ​ submitted by /u/Damampapoo [link] [comments]  ( 9 min )

Hi all, I'm very new to AI and have not used anything other than ChatGPT and NovelAI. I'm wondering if there's anything I can run privately where I can input a bunch of texts written by a specific author and then have the AI continue writing or write a new story in the style of said author? In Japanese btw. Again, very new. Not sure if I should have specific hardware requirements or anything like this. Google isn't showing me much either so I thought I'd ask here! submitted by /u/ItsCheif [link] [comments]  ( 9 min )

Alphabet’s (GOOGL.O) Google said on Thursday it does not see any change in its relationship with Broadcom (AVGO.O) following a media report the tech giant considered dropping the chipmaker as a supplier of artificial intelligence chips as early as 2027.[1] OpenAI’s ChatGPT can now “see, hear and speak,” or, at least, understand spoken words, respond with a synthetic voice and process images, the company announced Monday.[2] Amazon to Invest Up to $4 Billion in AI Startup Anthropic.[3] Spotify Will Translate Podcasts Into Other Languages Using AI.[4] Sources: [1] https://www.reuters.com/technology/google-discussed-dropping-broadcom-ai-chips-supplier-the-information-2023-09-21/ [2] https://www.cnbc.com/2023/09/25/chatgpt-speak-listen-process-images-openai.html [3] https://www.bloomberg.com/news/articles/2023-09-25/amazon-to-invest-as-much-as-4-billion-in-ai-startup-anthropic?embedded-checkout=true [4] https://www.forbes.com/sites/tylerroush/2023/09/25/spotify-will-translate-podcasts-into-other-languages-using-ai/?sh=65a05fa922ee submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Talk about a Grand Slam. Denny’s CEO Kelli Valade was joined Tuesday by NVIDIA CEO Jensen Huang to unveil a plaque at the Silicon Valley Denny’s where NVIDIA’s founders hatched their idea for a chip that would enable realistic 3D graphics on personal computers. “This is a place where we fuel ideas. Your story is Read article >  ( 6 min )

From gaming to creating to everyday productivity, NVIDIA RTX graphics cards feature specialized Tensor Cores that deliver cutting-edge performance and transformative capabilities for AI.  ( 7 min )

Announcements Top Stories In-Depth The post DSC Weekly 26 September 2023 appeared first on Data Science Central.  ( 20 min )

A podcast with Weimo Liu and Sam Magnus of PuppyGraph Open source Apache Iceberg, Hudi and Delta Lake have made it possible to dispense with the complexities and duplication of data warehousing. Instead of requiring time-consuming extract, transform and load (ETL) procedures, these large table formats make it simple to tap S3 and other repositories… Read More »Doing graph + tabular analytics directly on modern data lakes The post Doing graph + tabular analytics directly on modern data lakes appeared first on Data Science Central.  ( 20 min )

I am very puzzled as to the results I have observed today, after running an extremely simple environment and receiving really bad results. I am probably doing something wrong, and would like to ask for your wisdom to assist me in figuring out what I am doing wrong. I will not describe the entire task since that is a long story; I will just say that I started by doing something complex (a multi-objective reward), and when it failed I decided to try something extremely simple ("because it will surely work and I can proceed from there..."). To my surprise, the agent was not able to perform even that very simple task. That simple task is the following: at each step, choose a subset of items. Each item has a value, and the goal is to maximize the overall value (that is, at the end of the traje…  ( 10 min )

I've just started diving into the world of coding over the past week, and I've been using various tools like YouTube videos, Visual Basic, GPT-3.5, Bard, and Bing to help me learn the ropes. It's been a bit of a journey, and I've definitely picked up some understanding along the way, especially when it comes to libraries. But, you know, there's only so much you can really learn from AI models like GPT or other chatbots. Most of my progress has involved me taking bits and pieces of code I found here and there and trying to piece them together, even if it sometimes felt like making a digital spaghetti dish! One project I tackled involved using Stable_baselines3 PPO with ADAM optimization to play the classic game Flappy Bird. It was a bit of a wild ride, taking about 6-7 hours of my time, an…  ( 10 min )

I have to do masters project where a turtlebot3 needs to reach a goal position using DRL. I have managed to use move_base package to give global path. Local path planner just needs follow the path and reach goal. There will be dynamic obstacles too. I will give more information if you are ready. As mentioned I will pay for this. Let me know. I have one month left. Email ramanjeet995@gmail.com. Update : I should have said it like this. I need help with my project. submitted by /u/Pinball_1995 [link] [comments]  ( 9 min )

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers […]  ( 13 min )

Posted by Michał Januszewski, Research Scientist, Google Research The human brain is perhaps the most computationally complex machine in existence, consisting of networks of billions of cells. Researchers currently don’t understand the full picture of how glitches in its network machinery contribute to mental illnesses and other diseases, such as dementia. However, the emerging connectomics field, which aims to precisely map the connections between every cell in the brain, could help solve that problem. While maps have only been created for simpler organisms, technological advances for mapping even larger brains can enable us to understand how the human brain works, and how to treat brain diseases. Today, we're excited to announce that the Connectomics team at Google Research and …  ( 92 min )

I have a project that I think AI might be able to help with. I have access to thousands of vintage postage stamps which also have cancelation stamps from when they were mailed. I'm thinking of publishing a book, and I want to create clean reproductions of the stamp designs without the cancelation marks. If I train AI on high resolution scans of 10 to 40 identical stamps, is there a tool that can look for commonalities within the patterns and then remove conflicting artifacts (cancelation stamps, tears, and smudges)? I'm aiming for a 400% enlargement that shows off clean and accurate detail. What tools would I need? I’d prefer not to upload these scans to the web. Are there downloadable tools available? I’m technologically savvy and very experienced in graphic design software but have no experience in coding. All of this can be done manually in Photoshop, of course. But with thousands of stamp designs, I’d like to automate as much as I can. Thanks in advance for any insights! submitted by /u/fisheternal [link] [comments]  ( 9 min )

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The rapid advancements in artificial intelligence (AI) are causing concern as humans struggle to understand and control this evolving technology. Many people believe that since humans invented AI, they should be able to regulate and manage it for their own benefit. However, this belief is misguided as AI is a new and potentially dangerous situation that requires careful consideration. The author argues that AI is an evolutionary process that humans don't fully understand and cannot control. The latest developments in AI, such as large language models and deepfakes, are causing anxiety and raising questions about the future implications of this technology. Source : https://www.newscientist.com/article/mg25934573-800-ai-is-evolving-for-its-own-benefit-not-ours/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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China's AI industry relies on student interns who work as data annotators, performing crucial tasks to train machine learning models. These interns, recruited from vocational schools, face poor working conditions and subminimum wages. Recent regulations require employers to pay interns minimum wage and prohibit schools from assigning repetitive work. Tech giants like Baidu have partnered with vocational schools to create data annotation internships in less-developed regions, backed by local governments. The exploitation of student interns in China's AI industry raises concerns about labor rights and fair compensation. Source : https://restofworld.org/2023/china-ai-student-labor/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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I'm a researcher and for a "scientific outreach" event I will do a presentation for last year High School students with the subject "Artificial Intelligence and Neuroscience". My question is, do you know of a good introduction to the basic concepts of Artificial Intelligence for High School or freshman University level? The second part, related to Neuroscience applications, will be very targeted and will deal with contemporary clinical and research use cases. Thanks submitted by /u/aendrs [link] [comments]  ( 9 min )

I’m looking for some maybe less-known leaders in the space out on a mission to change something they care about- business, environmental, social, doesnt matter. I dont want it to be US-centric, so it would be nice to find some people all over. Feel free to send me links to articles 🧨 submitted by /u/Johnny_Whoop [link] [comments]  ( 9 min )

ChatGPT-4 Given the rapid advancements in the field of AI, the next few months are going to be pivotal. One of the pressing priorities is addressing ethical concerns. As algorithms become more pervasive, making sure they don't perpetuate biases or make unethical decisions is critical. That's not just a "good to have"; it's foundational. We can't build trust in AI systems unless we tackle this head-on. Another biggie is interpretability. Black-box models are potent, sure, but they're not very useful if no one can understand how they're making decisions. This has real-world implications—think medical diagnoses or judicial systems. So, there's a strong push towards making AI more transparent and understandable, not just for experts but for everyone. Computational efficiency is also high o…  ( 10 min )

Bangalore, often referred to as the Silicon Valley of India, has established itself as a global hub for technology and innovation. With a thriving ecosystem of startups, research institutions, and multinational corporations, the city has become a hotbed for artificial intelligence (AI) development. here explore the top AI companies in Bangalore, highlighting their contributions to the field of artificial intelligence and their impact on various industries. Read full article - Top Artificial Intelligence Companies In Bangalore submitted by /u/Techasoft16 [link] [comments]  ( 9 min )

I am searching for an AI that can scrape some Twitter profiles and make a daily recap about what they were talking about. Anything pops? Thanks a lot! submitted by /u/Alternative_Pea_4246 [link] [comments]  ( 9 min )

Examples: AI in Finance, etc- submitted by /u/premonial [link] [comments]  ( 8 min )

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https://twitter.com/AnthropicAI/status/1706202966238318670 https://preview.redd.it/hiymp9ctxcqb1.png?width=735&format=png&auto=webp&s=20cb3886710ee9a2a552b0fc881b8c96c0fc9208 submitted by /u/ShooBum-T [link] [comments]  ( 9 min )

I’m looking for subreddits and resources in general for Ai alignment. I recently read Life 3.0 by Max Tegmark and The Alignment Problem by Brian Christian. I was unaware so much was going on in the space. I am CEO of an AI startup, we have a compassionate AI. Most of the alignment focus out there is on superintelligence, little out there focuses on building modern day Ai that benefits humans. That’s been my focus for 17 years when I dreamed up what was possible and sent a proposal to executives at the mfaang corporation I was working at. I found out back then business doesn’t give a rats ass about Ai that actively makes its users life’s better. Well now I have proof that customers care. I’m hoping to find some resources on Ai alignment groups, conferences, more books etc. I’ll need to hire skilled ml engineers who do give a rats ass. Compassionate Ai isn’t just feel good tech. It would be good to do some networking. AI alignment doesn’t have to wait for AGI and super intelligence. It can and should be a focus today. My startup is proving it. Hoping to find a conference to network and share our research. submitted by /u/xyz_TrashMan_zyx [link] [comments]  ( 9 min )

I have one that I'd like to share with the class. I think the grokking / double descent paper was sponsored by cloud providers to get people to continue training even when the loss curve has flattened out (speaking as someone who is watching a flat loss curve all day today). Anyone have any other ones? I'm only being semi-serious obviously. submitted by /u/new_name_who_dis_ [link] [comments]  ( 9 min )

I'm not really a ML/backend guy, mostly work with front end but I want to be able to use chatgpt api with long term memory. I did some research and it seems like it's possible with vector databases but seems quite complicated to setup. Are there hosted solutions/api that would allow me to just have long term memory with chatgpt? submitted by /u/yalag [link] [comments]  ( 9 min )

Hi, we are doing a survey of ML deployment platforms. Kindly fill it out and share it with your friends. We will share the results with the community https://forms.gle/1Q3WeSukHj8xBzUBA submitted by /u/fazkan [link] [comments]  ( 9 min )

Looking for something that's lightweight on infrastructure/setup where I can quickly validate my use case quickly to see if I can achieve desired accuracy/precision with my initial data set, I've always done this manually in the past but was curious as to how others do it and if there are any tools designed for it or that support it submitted by /u/PatienceLogical2694 [link] [comments]  ( 9 min )

A magazine style article outline: Decentralizing AI: A Journey Towards True Collective Intelligence In today's digital age, AI shapes our interactions, decisions, and understanding of the world. Imagine a future where AI's guiding hand isn't controlled by a select few but shaped by the collective wisdom of people everywhere. Welcome to the horizon of decentralized training and alignment of Large Language Models (LLMs). Together, let's explore this visionary frontier. 1. The AI We Know Today At present, AI's most prominent representatives, LLMs like GPT-4, are a product of centralized training. Massive datasets, often sourced from specific regions or languages, direct their learning. While this method has given us incredibly powerful tools, it also raises concerns: potential biases, lac…  ( 12 min )

Hey, I'm the creator of MindMateGPT, an AI therapist that has helped a lot of people with emotional/social issues. It's not meant to replace humans, but it is a very useful augment as a daily therapy tool. It will refer you to a human if you have a very serious issue, but it provides a bunch of tools and coping tactics for every day emotional issues. Try it out! It's 100% free. submitted by /u/MindMateGPT [link] [comments]  ( 9 min )

Hello! I am a software engineer (4 yoe) working in full stack web and app development, transitioning to LLMs/ AI/ ML. My background includes extensive research in neuroscience so I am most comfortable w academic or comprehensive lectures. I am looking for reputable and vetted lectures, talks, resources on ML and the like. I am having trouble sifting through the surface level pop sci type resources floating around on the internet. I’m NOT looking for the 10min everything you need to know about AI type talks. Thanks in advance! submitted by /u/yosoylatte [link] [comments]  ( 9 min )

this is mostly a cross-post from r/rust as my initial attempt failed because of some external links We've first announced Candle, a minimalist ML framework in Rust 6 weeks ago. Since then we've focused on adding various recent models and improved the framework so as to support the necessary features in an efficient way. You can checkout a gallery of the examples, supported models include: Large language models: LLaMA, LLaMA v2, Falcon, Phi-v1.5, StarCoder. Quantized models with the llama.cpp approach: LLaMA, T5, Phi-v1.5. Image generation: Stable Diffusion, Wuerstchen. Computer Vision: DINOv2, yolo-v3, yolo-v8, Segment-Anything Model. Text-to-speech: Whisper. One of the big upside of the pure Rust approach is that models can run directly in the browser using WASM, these can be accessed through this collection, you can try out Yolo, Whisper, Segment-Anything, T5, Llama2-c from your web browser. Finally, in order to present a use case where Candle has unique capabilities, we've built a quantized version of the recently released Phi-v1.5 LLM. You can try it out with the following command, this uses a q4k quantized model resulting in very fast inference on CPU while still producing pretty nice texts. $ cargo run --example phi --release -- \ --prompt "Explain how to find the median of an array and write the corresponding python function.\nAnswer:" \ --quantized --sample-len 200 Explain how to find the median of an array and write the corresponding python function. Answer: The median is the middle value in an array. If the array has an even number of elements, the median is the average of the two middle values. def median(arr): arr.sort() n = len(arr) if n % 2 == 0: return (arr[n//2 - 1] + arr[n//2]) / 2 else: return arr[n//2] It's still very early days for Candle so please let us know if you start using it and run into some rough edges. We look forward to Rust getting a lot more usage in the ML space! submitted by /u/l-m-z [link] [comments]  ( 10 min )

Hi, I'm reading this GAN paper which introduces a faster and simpler GAN architecture for creating high resolution images: FastGAN paper The authors claim the computational advantage of their approach is partly the simple architecture. Looking at figure 3 and 4, I cannot quite see why this is considered a simpler architecture to older GANs,say DCGAN. I get the technical argument that the generator only has one convolutional layer, but figure 3 has lots of up sampling layers, and the discriminator has plenty of downsampling layers. So why is this considered a simple GAN model? Any pointers appreciated. submitted by /u/treetop-600 [link] [comments]  ( 9 min )

I just wrapped up a new blog post about our experience working with LLMs while developing InfraCopilot. We're using both GPT4 and GPT3.5-turbo(16k) differently compared to what others are doing. We found a few new LLM tricks like dynamic examples and automated e2e intent parsing testing that I think are applicable to many other teams. I'd love to hear what everyone thinks, have you tried using dynamic examples while working with LLMs? submitted by /u/lothamer [link] [comments]  ( 9 min )

First of all anyone offended by that question - heartiest apology. I am using it myself profusely at the moment. The reason for me asking this question, over last few weeks / months, I have been gradually educating myself in machine learning using Tensorflow and have been able to train multiple models using only one of the model zoo candidates. All the other pre trained models have failed me so far. I went onto Tensorflow official forum / Stackoverflow / Tensorflow github with specific error messages that I am getting on Ubuntu with Nvidia card / Mac M2 and there has been absolute radio silence in response to multiple posts over last month. Found many open issues listed since 2020 on the same line as mine i.e. identical error messages that people have come across. Finally after about a month of being on TF forum, I direct messaged an official TF2 dev who kindly responded with answers. I haven't succeeded yet with any of the pre trained model from the official section. Only one model from research section is working so far for me i.e. Faster_rcnn_resnet_50_640x640 .. Thus the question. Kindly help me enlighten myself with where is this thing headed. Should I consider switching to Pytorch or some alternative ? If yes what alternatives do you recommend ? TIA submitted by /u/dpadhy [link] [comments]  ( 9 min )

Hey! Months ago, I was fascinated by Karpathy’s nanoGPT project - the ability to train a small LLM on your text file seemed very interesting to me. I tried training it on my chat history to build some inifinite chat-generator for fun, but unfortunately, the results were bad. Recently I had even worse experiences with newly-released ChatGPT 3.5 fine-tuning. Are there any good, simple ways to train/fine-tune LLMs now? I would love something that could train on an Apple M2 processor (like Karpathy’s nanoGPT), or Colab, or cheap API (like ChatGPT fine-tuning). submitted by /u/11igor [link] [comments]  ( 9 min )

As software projects grow, changing code across entire repositories becomes tedious & error-prone. Tasks like migrating APIs or updating dependencies require complex edits across files. I explored a new approach from Microsoft Research to automate these "repository-level" coding challenges with AI. Their new paper proposes CodePlan - an AI system that breaks repository tasks into incremental steps guided by planning & analysis. Key points: Uses LLMs like GPT-3 for localized code edits Maintains validity across repository via incremental analysis Adaptively plans multi-step changes based on code dependencies Significantly outperformed baselines on API migration & temporal edits Automated tasks across 168 file C# codebase 2-3x more accurate edit locations than baselines Produced final valid codebases, unlike reactive approaches The core insight is combining LLM strengths with rigorous planning based on dependency analysis. This automates interdependent code changes that naive LLM use struggles with (I personally have these kinds of issues all the time with GPT4 - lack of context about the entirety of the repo/how files fit together). I think CodePlan demonstrates AI can expand beyond small coding assists into large-scale engineering tasks. Planning + LLMs > LLMs alone. This could really improve productivity and code quality... at least for me :) Full summary. Arxiv paper: https://arxiv.org/pdf/2309.12499.pdf submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

In the main scenario, the smaller model learns from the same data as the bigger model and also from the predictions of the bigger model and incorporate the 2 output labels on a specific loss. Basically, it is equivalent to say to the smaller model : "be careful this example is hard" in the case that big model divergence from true output? I am missing something? submitted by /u/Grumlyly [link] [comments]  ( 9 min )

I made a FOSS baby sleep tracking system. The system tracks wake/sleep status, and informs the user when their baby is likely to need a nap next. But it stopped working as soon as my baby started sleeping on his stomach, and started using blankets. The original version relied on anatomical features being visible. This version delivers the ability to train a blank slate SVM binary classifier on pictures of a user's baby, making it extremely biased (and resilient) to the custom behaviors the user's baby exhibits (blanket covering baby, teddy bear/other objects in crib, etc.). All generated data stays on your machine, nothing leaves the LAN. Video: https://youtu.be/8i0wHA_knKc?si=uhA4PpOYP0jMKLz1 For obvious reasons I didn't have a dataset of babies sleeping.. so I wrapped a python/flask service with a React app which lets a user press a button to train the model w/ a new image from the camera's live stream. Then this model is invoked over time (+ other heuristics) to determine whether your baby is present and sleeping. I believe it works better than $300+ systems sold on the market, open sourced it: https://github.com/calebolson123/BabySleepCoach ​ I'm thinking a fun next step for this project could be to apply privateGPT on the feature-engineered sleep records for a true "Sleep Coach" submitted by /u/GoochCommander [link] [comments]  ( 9 min )

Is there a Granger test where series are a quarterly one and a weekly one? submitted by /u/Pineapple_throw_105 [link] [comments]  ( 9 min )

Hello everyone, I'm conducting a research survey on the challenges and gaps in AI security. Given the expertise in this community, I believe your feedback would be invaluable in shaping the future of AI security solutions. The survey takes less than 10 minutes and delves into current practices, perceptions, and needs related to AI security. If you have experience or insights in this area, I would greatly appreciate your participation. Survey Link: https://forms.gle/i9AefyL8izyt9QjX6 All responses will remain anonymous, and the collected data will only be used for research purposes. Additionally, if you're open to a deeper discussion on this topic, there's an option within the survey to indicate your interest. Thank you in advance for your time and insights! If you have any questions or additional thoughts, please don't hesitate to comment below. submitted by /u/Agile_Temperature678 [link] [comments]  ( 9 min )

The Wikipedia page on Diffusion Models has been pretty minimal for an entire year. I feel like it should be fixed, so I fixed it finally. It strikes me odd that such a hot topic has such atrociously bad Wikipedia. I feel duty-bound to educate the near-future AI, since they'll be reading Wikipedia for the next few years at least. Currently I think it's mostly complete, but I still don't understand the mathematical details of DDIM (I tried reading the paper and could not understand it), or generally how it is possible to sample without noise. This is a serious problem since as far as I see most of practical diffusion models use deterministic sampling, and they are all based on the same principle as DDIM. If anyone could explain simply what DDIM is really doing that would be great. I understand part of the paper: that they constructed an entire family of distributions over trajectories that has the same two-point marginals. I also haven't got much in the section on Examples. If you think there are some interesting examples of Diffusion Models, please comment below. submitted by /u/furrypony2718 [link] [comments]  ( 9 min )

For the data scientists/applied scientists/research scientists - What kind of projects are you working on now that the economy has shifted and companies are focusing more on profitability than on growth? What techniques have worked for you and what are you looking into as potential solutions? An example would be - optimizing your marketing campaign spend in channels that give you the most bang for your buck vs just spending arbitrarily to acquire new users. submitted by /u/Terrible-Hamster-342 [link] [comments]  ( 9 min )

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I created an OpenGL/OpenGLES based inference framework a while back which is rather GPU-agnostic and might be a good option for distributing multi-platform ML solutions for platforms ranging from Android over desktop to WebGL(2). Quite recently I added support for LLMs to that (restricted to 4-bit quantized Llama models for now). The LLM-enabled fork can be found here (compileable sample code inside). Maybe someone finds this useful. Also looking for collaborators to extend the functionality. ​ submitted by /u/mtnwrw [link] [comments]  ( 9 min )

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hi everyone, i’m working on a project in which i get a list of orders (id,delivery coordinates, delivery deadline). i need to deliver all packages while putting in consideration clients priority and taking least time and distance as possible. so the goal is to sort the orders to achieve the target. what would be the action / observation space for such environment? and how can i define the step method to perform this scenario? submitted by /u/overflow74 [link] [comments]  ( 9 min )

I’m new to ML and RL, and I’m building a small piece of code using gymnasium to be able to use mujoco. Specifically, I use Mujoco’s Humanoid, here is my code (https://github.com/NghiaPhamttk27/Humanoid).I use some algorithms in RL like SAC, TD3, A2C from stable\_baselines3. After every 25000 TIMESTEPS, I will save my model in the models folder. When I train continuously, everything goes well, I can see it on the tensorboard graph. But when I continue training a model that has been trained, the graph suddenly drops. In the image below you can see that I retrain the models at SAC\_75000 and SAC\_100000 and SAC\_125000. The graph continuously decreases at those thresholds.I think something happended Can you give me a reason or share something with me? Thanks in advance. You can see graph of SAC goes down when 76k, 100k, and 125k timeSteps submitted by /u/Nghiattk27 [link] [comments]  ( 9 min )

Am starting out working on an RL problem and am wondering what people generally use to implement the algorithms? I’ll need to build a custom environment, but I assume I can subclass something from Gym. submitted by /u/suds_65 [link] [comments]  ( 9 min )

This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove […]  ( 9 min )

We’re at an exciting inflection point in the widespread adoption of machine learning (ML), and we believe most customer experiences and applications will be reinvented with generative AI. Generative AI can create new content and ideas, including conversations, stories, images, videos, and music. Like most AI, generative AI is powered by ML models—very large models […]  ( 12 min )

Hello! I am a software engineer (4 yoe) working in full stack web and app development. I was a neuroscience researcher until I switched to software dev and now I am transitioning back into the intersection of neuro and software. I am looking for reputable, vetted, and comprehensive lectures, talks, resources on LLMs and the like. I am having trouble sifting through the surface level pop sci type resources floating around on the internet. I’m NOT looking for the 10min everything you need to know about AI type talks. Thanks in advance! submitted by /u/yosoylatte [link] [comments]  ( 9 min )

E-commerce has improved technology and convenience for consumers globally. Fraud is a problem in e-commerce. Merchants and platforms fight fraud to protect their businesses and customers. Anomaly detection is a powerful tool for identifying irregular patterns and potential fraud. This article explores how anomaly detection is used in fraud detection for e-commerce and discusses different… Read More »In fraud detection for e-commerce: How does anomaly detection fit in and what are the key approaches? The post In fraud detection for e-commerce: How does anomaly detection fit in and what are the key approaches? appeared first on Data Science Central.  ( 22 min )

Thanks to the internet, you can now easily expand your reach and engage with diverse audiences wherever they are. However, this opportunity raises an important question: how can you localize your web content and maintain the security and privacy of sensitive data? This article comprehensively explores the best practices that will help you maintain data… Read More »The essential guide on data security and privacy in web localization The post The essential guide on data security and privacy in web localization appeared first on Data Science Central.  ( 22 min )

A major aspect of ongoing data center redesign is due to AI’s massive, complex workloads and the need to add many more graphic processing units (GPUs), tensor processing units (TPUs) or accelerators to the mix. The power these units require and the heat the units generate have forced designers to rethink what constitutes a feasible… Read More »How AI growth has triggered data center redesign The post How AI growth has triggered data center redesign appeared first on Data Science Central.  ( 20 min )

Microsoft researchers are introducing AutoGen, a framework for simplifying the orchestration, optimization, and automation of workflows for large language model (LLM) applications—potentially transforming and extending what LLMs can do. The post AutoGen: Enabling next-generation large language model applications appeared first on Microsoft Research.  ( 10 min )

In the wake of ChatGPT, every company is trying to figure out its AI strategy, work that quickly raises the question: What about security? Some may feel overwhelmed at the prospect of securing new technology. The good news is policies and practices in place today provide excellent starting points. Indeed, the way forward lies in Read article >  ( 7 min )

We are beginning to roll out new voice and image capabilities in ChatGPT. They offer a new, more intuitive type of interface by allowing you to have a voice conversation or show ChatGPT what you’re talking about.  ( 4 min )

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Hi guys , TorchClassifierData is A small pytorch utility to Import, Split ,Normalize and Visualize custom dataset for classification tasks. wich is indispensable for real word problem . You can find a full notebook that use TorchClassifierData to train a classifier on this kaggle dataset here. The code source is avalaible on my github. Thank you. submitted by /u/charles_data_dev [link] [comments]  ( 9 min )

Diffusion models are basically Langevin sampling. What are the key differences and tricks that set them apart from Langevin dynamics? Why do they work so well while very similar sampling methods don't? submitted by /u/Dangerous-Flan-6581 [link] [comments]  ( 8 min )

​ https://preview.redd.it/cczbhu367aqb1.png?width=1600&format=png&auto=webp&s=f1761911d7ce3bbefaef43774b5d60f638886893 ML is often portrayed as a magical field where anyone with a laptop and Python skills can build amazing AI systems. The reality is less democratic: mastering ML requires gritty, systematic work best learned through formal training. You need rock solid foundations in math, programming, and core concepts—skills acquired through advanced education, which (almost always) is beyond self-taught hackers. Most think a PhD is unnecessary, but the reality is that advanced degrees provide the deepest training. Patience and persistence do matter, but a PhD gives you a 10-year head start over weekend warriors. The hype overlooks the work and education needed to excel. This article has a great blueprint of all the required skills to become a ML Engineer (in the CV field). submitted by /u/btcmx [link] [comments]  ( 9 min )

So briefly about my current experience, I graduated 2 years ago with a bachelor in data science and I have 2-3 years of experience as a data scientist/ml engineer/software engineer. So I’ve got competing offers, one from the big 4 accounting firms as a software systems engineer - AI/ML (Big 4) and the other as a machine learning engineer. The startup salary is higher while big 4 is lower. Additionally the startup isn’t necessarily a unicorn it’s a relatively small startup with an interesting product but it doesn’t necessarily blow me away. The salary at the startup is 15 percent higher that that of the big 4 offer. For those wondering I did already negotiate the salary and they did increase it marginally. I am conflicted because I think that the big 4 jobs will have 1) more career growth 2) more potential future opportunities and 3) more networking potential Is this an accurate assessment? Which is the best job to take for maximum future potential? submitted by /u/zorenum [link] [comments]  ( 9 min )

So I've been thinking of implementing SwinBert for a college project and have been wondering what all resources i would be needing for a computer. Any ideas? submitted by /u/Big-Brain_69 [link] [comments]  ( 8 min )

I’ve been having a bit of an existential crisis as of late and wanted to ask for advice on how to move forward. I have a Master’s in CS with research experience and a few publications applying machine learning in a fairly niche area (So not novel from the ML side). Since graduating, I’ve worked ~2 years as an ML engineer in small company(Niche area, different than research). I’ve done quite well here and have played a critical role in taking several big greenfield projects to completion. Most of my work is framing problems, understanding what’s possible with current research, then building the data pipelines, and training models(with small mods here and there). My main worry is that I might be approaching a point where there won’t be any more problems I’m capable of solving here. I’d imagine I’d hit the same wall at any future company with my current skill set. I’d like to continue working in CV/Graphics/Perception if possible, but I’d also like to be realistic about the competitiveness of this particular subfield and my general ability. I’ve been trying to up-skill and am struggling to self study MVG by Hartley and Zisserman. I’m also looking into OMCS to review low-level programming - maybe I can transition to optimizing ml/cv algorithms? It hit me recently that I don’t really know what to study/what I’d benefit from learning. Hence this post. Any advice would be most appreciated! submitted by /u/answersareallyouneed [link] [comments]  ( 9 min )

I'm curious if anyone has tried pix2struct-large for web-scraping text from wesites. If so - how well did it perform? If not - is there something else that is considered better to use? submitted by /u/ReddSpark [link] [comments]  ( 8 min )

Hypothesis: Big tech companies are investing vast amounts of money to develop general models on which others will build. They'll develop interfaces to make it easier for others to fine-tune on top of their models. So that there will be less and less of a need for ML engineers that know how to create a deep learning model in Pytorch, and more and more of a need for data engineers that simply plug into pre-trained models. An AI assistant will also be quicker at coding up a more bespoke AI model for a companies needs, guided by data engineers. What do people think? Is this a scenario that they think will play out? Where will the demand for AI skills be coming from in the future? submitted by /u/QuintBa [link] [comments]  ( 9 min )

LLMs are great with words but can't handle visual tasks like understanding images. Teaching them to use visual tools could make them much more capable. A new paper introduces GPT4Tools - a method to efficiently teach existing LLMs to invoke tools for visual tasks without proprietary data. My highlights from the paper: Uses ChatGPT as a "teacher" to generate instructional data for other LLMs Fine-tunes LLMs like Vicuna on this data using selective weight tuning (keeps base model frozen) Allows smaller 13B LLM to match 175B GPT-3.5 on seen tools after tuning Data augmentation with negative/context samples was found to be the secret sauce to get this to work Can generalize to brand new visual tools in a zero-shot way This is big because it shows we may not need hyper-expensive training of massive models to impart visual capabilities to LLMs. They seems to be generalizable enough that they can be taught to work with images. Some examples shown include counting objects or segmenting items in pictures using other tools. With this approach, existing models can be made multi-modal! Pretty cool. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hey all, I run a small team & we are collaborating on a few data sets that we use to fine-tune GPT3.5, We are currently using Google Sheets and I'm wondering if there is a tool where we can organize our data preferably with version control Any ideas? submitted by /u/zeJaeger [link] [comments]  ( 9 min )

Hello, I have created point clouds from images using Point-E. Sadly they are very sparse (for example wehn inputting an image of a house, the roof has very few points in it) and I was searching for other Models, that could make the PC more dense and predict the color of every point. Point-E outputs xyz and rgb vectors for every point. Do some of you have advise for me here? submitted by /u/bySmily [link] [comments]  ( 9 min )

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Hi I'm a student and working on a RL project for the university and need some guidance. I have created a 3d model with mujoco (I have the xml file) how do I create an environment in gymnasium with this xml file? for the sake of an example let's say I have the xml file of the humanoid model how do I load this in gymnasium so that I could train it to walk? (this is just an example because the current project is harder to explain, but will use the humanoid model in the project) or is the approach that I'm trying is not appropriate at all? I came across this stackoverflow post where they say mujoco is itself good for this but was hard for me to understand due to lack of examples. would really appreciate some advice and guidance thank you. submitted by /u/rakk109 [link] [comments]  ( 9 min )

I just open-sourced (MIT License) semantic segmentation annotation tool powered by segment-anything model that I used for a while in my projects. Hopefully it will help someone as it seems to me that it is more suitable for small projects than popular huge web based annotation tools. Link to the project: SAMAT (any feedback in Discussions section on GitHub is appreciated) Features: Brush annotation (opposed to polygons) Magic Wand (like in Photoshop) powered by segment-anything masks (it is optional, if you don’t have cool GPU to prepare masks) samat showcase Why yet another annotation tool? Before starting this project I tried supervisely, segments.ai, roboflow and several others, but found them not convenient for my tasks. Their cons, I tried to fix with my tool: Latency: they are web based, hence burden with irritating latency during labeling (SAMAT is snappy because it is a local desktop app) Complicated: too much features, hence overloaded UI (SAMAT is just a colored brush) P.S. there is another labeling tool called SALT on github which also uses segment-anything model, but it follows different approach to UI/UX, may be it will be more suitable for you, so take a look at it too. submitted by /u/Divelix [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.11672 Abstract: In this research, we explore the efficacy and potential of Generative AI models, specifically focusing on their application in role-playing simulations exemplified through Spyfall, a renowned mafia-style game. By leveraging GPT-4's advanced capabilities, the study aimed to showcase the model's potential in understanding, decision-making, and interaction during game scenarios. Comparative analyses between GPT-4 and its predecessor, GPT-3.5-turbo, demonstrated GPT-4's enhanced adaptability to the game environment, with significant improvements in posing relevant questions and forming human-like responses. However, challenges such as the model;s limitations in bluffing and predicting opponent moves emerged. Reflections on game development, fi…  ( 9 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 9 min )

For those who have tried Microsoft's Arsenal tool in conjunction with MITRE's offerings, how does it compare to other AI security tools you've used? submitted by /u/Agile_Temperature678 [link] [comments]  ( 8 min )

so I've had this problem where I try to train the model in RVC but the training stops after: INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration. Does anyone know why is it happening and how can I fix it? this is what it shows to me and idk what to do... any help would be appreciated thank you submitted by /u/mannequin7412 [link] [comments]  ( 9 min )

Our company has a new data science team and the team is relatively in experienced. I am working on a regression ML project and want to deploy it using best practices.What materials are there for learning how to implement CI/CD pipelines that deal with data transformation/model building/testing/deploying? The company uses azure environment with databricks/azure devops setup. I appreciate resources that show examples on how to setup MLOps on the company's environments. submitted by /u/userid95 [link] [comments]  ( 9 min )

[D] I watch this MIT lecture and I don't know if my understanding of wx + b = 0 is correct or not. Every explanation of the hyperplane state that all the points which are orthogonal to the vector w are in the hyperplane. However, all data point coordinates are defined wrt origin. So in order to attain this objective, we define the hyperplane as : wx = c This vector w is the vector which is normal to the hyperplane. And the data points x when dot producting with w outputting c are the points which are in the hyperplane due to the coordinates representation of x wrt origin. So points in the hyperplane are the points which when performing dot product with w equals 0 when the coordinates is defined wrt origin that lie in the hyperplane is equivalent to the points which when performing dot product with w equals c when the coordinates is defined wrt original origin (0) or (0,0) or ... submitted by /u/Emotional-Fox-4285 [link] [comments]  ( 9 min )

Hi, I have a fairly good understanding of how LLMs work under the hood, the attention mechanism, the different architectures and so on. However most of that knowledge takes the backseat in practical cases, especially in the industry. Are there any resources which discuss practical LLM engineering and the challenges that come with it? I'm talking about everything from fine-tuning to dealing with tokenisation limit to optimising the vectorDB and so on? submitted by /u/thoraway0612 [link] [comments]  ( 9 min )

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Hello to everyone. I'm lookin for some good AI github project to convert the language spoken in a video to a different language,like heygen / labs / video translate,but free. I mean,I can't afford prices like those and I think a few of us can,but I'm sure that the developers at heygen took some project born and hosted on some github and they have improved it. Would someone share some of those github projects ? thanks. submitted by /u/loziomario [link] [comments]  ( 9 min )

Hi there. This is a wonderful sub, and it's hard to please everyone on SUCH a broad topic... but it's intriguing to think about, as a lifelong magazine fan (and websites of course) - about using AI to filter, craft and create an amazing website/magazine ABOUT AI - well-categorized, maybe also human/editor curated/tweaked, but as fresh as ... well, the artificial sub but out of the Reddit bold/design into something ... well, something I'd read, visit a lot and love to help make work as a writer/editor (on the side of my "real" all-consuming gig of course;-) After all, if AI can 'make great code'/create great websites, maybe some folks out there are already trying to make this happen, for ease of information availability and organization. I can picture the departments/sections/categories now - I bet you can too! Anyone gone very far down that road yet (maybe the folks already doing the PC Magazines of the world) of organizing the vast fast-moving info beyond the AI Brews, Ben Parr's AI Analyst, etc.? Fun or a life-long journalist and tech geek (but not a coder, gamer etc.) to think about, at least! submitted by /u/barneylerten [link] [comments]  ( 9 min )

LLMs are great with words but can't handle visual tasks like understanding images. Teaching them to use visual tools could make them much more capable. A new paper introduces GPT4Tools - a method to efficiently teach existing LLMs to invoke tools for visual tasks without proprietary data. My highlights from the paper: Uses ChatGPT as a "teacher" to generate instructional data for other LLMs Fine-tunes LLMs like Vicuna on this data using selective weight tuning (keeps base model frozen) Allows smaller 13B LLM to match 175B GPT-3.5 on seen tools after tuning Data augmentation with negative/context samples was found to be the secret sauce to get this to work Can generalize to brand new visual tools in a zero-shot way This is big because it shows we may not need hyper-expensive training of massive models to impart visual capabilities to LLMs. They seem to be generalizable enough that they can be taught to work with images. Some examples shown include counting objects or segmenting items in pictures using other tools. With this approach, existing models can be made multi-modal! Pretty cool. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

A recent paper challenges assumptions about the energy use of AI models, finding that AI systems emit significantly fewer carbon dioxide equivalents (CO2e) compared to humans when producing text or images. The authors emphasize the importance of measuring carbon emissions from AI activities to inform sustainability policies. The ongoing debate among AI researchers highlights the challenges of accounting for the interactions between climate, society, and technology. Source : https://venturebeat.com/ai/how-much-energy-does-ai-use-compared-to-humans-surprising-study-ignites-controversy/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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I've recently started working on a project to create an entire episode of a cartoon show that stopped airing a while back. I've run into some trouble though in finding a program that can accurately recreate the character. Does anyone know what app or website I could use for this, or am I coming at this all wrong? submitted by /u/therabbitinthehat2 [link] [comments]  ( 9 min )

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Within the next two to three years no code AI co-pilots will enable exponentially more people to enter the AI app marketplace . The opportunity to create apps without needing to know how to code or hire a technical team to build them is a powerful game changer that will vastly expand the field. Many of the most pressing problems of our world - ripe for revolutionary new AI innovations - can in a very real sense be described as sociological and psychological in nature. For example, It has been said that climate change is much more of a moral issue than a technological one. Once we summon the will to address climate change, we will do what needs to be done. What this means is that sociologists, psychologists, anthropologists, economists and other social scientists will very soon be able to…  ( 10 min )

Couldn't find anything that works submitted by /u/Marvellover13 [link] [comments]  ( 8 min )

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In the previous post I mentioned that a particular Mersenne prime would be unsuitable for cryptography. In fact, all Mersenne primes are unsuitable for cryptography. A prime number p is called “safe” if p = 2q + 1 where q is also a prime. Safe primes are called safe because p − 1 does not […] Mersenne primes are unsafe first appeared on John D. Cook.  ( 6 min )

Electronic computers were invented before public key cryptography. Would public key cryptography have been possible before computers? The security of RSA encryption depends on the ratio of the difficulty of factoring relative to the difficulty of multiplication. This ratio was high, maybe higher, before modern computers. Suppose the idea of RSA encryption had occurred to […] Victorian public key cryptography first appeared on John D. Cook.  ( 6 min )

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​ slide as part of the presentation. I've listened to the entire lecture and am now going through these slides and I didn't understand the solution provided for the choices above in the image. So far I understand it like this. Noise basically means that the agent will not always do the action that you say. You say left and it will go left only 50% of the time. it will choose other actions randomly. so, Why is the answer to the (c) and (d) questions (2) and (3) respectively? I understand the learning rate but not how risking the cliff is affected by the noise. submitted by /u/vestedpolecat [link] [comments]  ( 9 min )

https://pepe-web3.network submitted by /u/Beginning_Success208 [link] [comments]  ( 8 min )

For a potential school project, im currently exploring whether or not some of the success the transformer architecture has had in seq-to-seq applications and high-dimensional pattern recognition could be extended to certain (online) RL problems (mainly those with high dimensional environment as well as long-term planning). This could be done by using an augmented transformer as a function approximator, probably in combination with a SOTA online-rl algorithm (such as PPO, but if you know something that generally performs better do tell). Due to some of the problems associated with highly complex models and sample efficiency, I also thought about training the model using imitation learning first (which should be no problem with policy gradient methods afaik, though some slight adjustments would have to be made). For context, I'm thinking on benchmarking the approach using AlphaStar unplugged. However, when looking at current literature, only very few papers directly use transformers this way. Transformers seem to be very sample efficient and to generalize very well, but they are still only really used in a purely offline context (sometimes without directly using RL-techniques, such as with the Decision Transformer). And, if they are used in an online context, then only in some really intricate combination with other models (such as in AlphaStar). Is there a reason why the approach I am currently considering is not popular in literature? Thank you very much. submitted by /u/Omycron83 [link] [comments]  ( 9 min )

ChatGPT, Bard, GPT-4, and the like are often pitched as ways to retrieve information. The problem is they'll "retrieve" whatever you ask for, whether or not it exists. Tumblr user @indigofoxpaws sent me a few screenshots where they'd asked ChatGPT for an explanation of  ( 3 min )

AI Weirdness: the strange side of machine learning  ( 2 min )

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AI systems like large language models (LLMs) are good at generating sentences but do not understand the meaning of the language. LLMs have shown emergent abilities and can be used as aids to brainstorming. GPT-4, an LLM, has been found to beat humans in creativity tests. In an experiment, GPT-4 generated more, cheaper, and better ideas for a product than human students. A professional working with GPT-4 can generate ideas at a rate of about 800 ideas per hour, making them 40 times more productive than a human working alone. This technology is seen as a potential tool for corporations, similar to management consulting firms like McKinsey & Company. Source : https://www.theguardian.com/commentisfree/2023/sep/23/chatbots-ai-gpt-4-university-students-creativity submitted by /u/NuseAI [link] [comments]  ( 9 min )

I've got a use case where I have dozens of PDFs which contain information applicable to my job. I'm wondering if there is a tool that can search through them all at the same time looking for answers to questions that I type. And once it finds something, pull up the location so I can read further. It should be restricted to the information I give it. I've heard a lot of the large language models like chatgpt and claude can do this, but they are restricted in the amount of files I can upload. submitted by /u/Aggressive_Ad_507 [link] [comments]  ( 9 min )

TLDR: New training approach enables smaller AI models to achieve state-of-the-art translation performance Large AI models like GPT-3 have good performance on translation tasks, but some smaller models struggle. Researchers from Johns Hopkins and Microsoft propose a new 2-stage fine-tuning method called ALMA that unlocks stronger translation abilities in smaller models with just 7-13 billion parameters. How it works: Fine-tune on monolingual data in non-English languages to improve comprehension Further fine-tune on small sets of high-quality human-translated parallel text The authors claim this achieves SOTA-level translation using far less data and compute than conventional methods: Matches performance of 175B parameter GPT-3 and 54B parameter NLLB with only 7-13B parameters Reaches NLLB-level quality with just 1 billion monolingual tokens and 18 hours of training I think this shows that smaller models can reach SOTA translation with specialized fine-tuning, so we may not need endlessly bigger datasets and models to get better performance. Looks like deliberate tuning targeting key language skills could be more important. Full summary here. Paper (preprint) is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Yes, I know this topic has been beaten to death but entertain me (no pun intended) for just a few minutes. So yes, it's obvious that we have pretty advanced AI-powered applications that can generate images, music, short stories, hell even objects for video games. I'm curious as to how crazy this is gonna get in the coming decade or even shorter like the next 4 to 5 years. I mean look at AI-generated images now, they're getting more and more sophisticated across various different styles of art. I think it's only a matter of time where you could take a certain image of a character or something tell the app "Hey make the same image but make the character's arm raised slightly to the left here" and bam all of a sudden you have an animation (and this may already be possible). Add to that AI-ge…  ( 10 min )

Hi everyone 👋🤗 Iv made a short ai documentary Here is a small part of it Hope you enjoy it For the full clip you can check out https://youtu.be/uIdb5VELpio?si=uvqiw0hyTyPBHtjP submitted by /u/DigitalEffectsAI [link] [comments]  ( 8 min )

https://www.louisbouchard.ai/research-papers/ In this article are shared the best tips and practical tools I use daily to simplify my life as an engineer/researchers to be more efficient when looking for interesting research papers and reading them TLDR the tools discussed: - 42 Papers — Find trending papers - Connected Papers — Create a visual graph with your paper’s citations’ relations. - CatalyzeX — Get code for papers directly on Google, Arxiv, Scholar, Twitter, and more - Daily Papers — Find trending papers on Twitter - Papers With Code — Find papers for your task with code! - Crossmind — Video explanations for many Arxiv papers - Yannic Kilcher — Great youtube channel covering AI papers - What’s AI— Great youtube channel covering AI papers - Letitia — Great youtube channel covering AI papers - Two Minute Papers — Great youtube channel giving a quick overview of AI papers ​ Please, let me know if you use any other tools that I did not mention in my article that could be of great addition? submitted by /u/MLtinkerer [link] [comments]  ( 9 min )

I'm often using Chat GPT or Bing (Bard is not her available in my country) when I'm looking for something that is relatively complex in the scientific or technical field and I don't have enough background to make a keywords research or when the question is too complex for it. Then, for scientific topics, I try to get a confirmation with Google using the keywords in the generated answer and google. For technical topics I can often check if it works. Some examples: For cooking, a topic I don't master enough to ask questions with the best keywords... Are the spicy compounds of Ginger soluble in water or oil ? > I will get me to know that "the gingerol is liposoluble", what can be more easily checked in Google. Or for 3D geometry, a topic where I have complex questions... I want to place a 3D frame B so that the Z axis of the B frame point towards the world origin A. I know the angle Alpha between the Z axis of the frame B and The X,Y plane of A. I know the angle Beta between the X axis of A and the vector that goes from the center of A towards the projection of the center of B on the X,Y plane of A. How do you compute the homogeneous rotation matrix of B relative to A, knowing Alpha, Beta and the distance between the center of A and the center of B ? Most of time, Bing and the free ChatGPT can answer. But sometimes they struggle or give incorrect answer. Would ChatGPT plus paid subscription be helpful to find more accurate answers? submitted by /u/x11ry0 [link] [comments]  ( 9 min )

Sure, here's a comedic take on the concept of free will versus determinism, framed as a conversation between God and a human named Bob. Scene: Heaven's Control Room—Giant monitors display various life paths and decision trees. God: (looking over the monitors) Ah, Bob, welcome to Heaven's Control Room! This is where the magic happens. Or should I say, determinism? Bob: Wow, this place is amazing! But wait, why are we talking about determinism? I thought I had free will to make choices in my life. God: (chuckles) Ah, free will! Humanity's favorite illusion. Have a seat; we need to go over a few things. Bob: Illusion? I chose to wear mismatched socks today, didn't I? God: (grinning) You think that was a free choice? Ever heard of quantum mechanics? Your socks are mismatched at the sub…  ( 10 min )

Hi everyone, been playing with tortoise all day and trying to get a clone of my voice as close as possible and nothing seems to work. I'm thinking maybe I need to use my phone to record my voice and pass it into tortoise but I'm not sure of how to get the wav files from my phone at the recommended quality. Has anybody had really good luck getting tortoise to mimic you very closely? submitted by /u/MaxxMarketTrades [link] [comments]  ( 9 min )

I'm using LGBM for regression, where the Target column's lagged values (7 columns for each lag day) are also used as predictors when training the model. Absence of the 7Day lag values severely increases MAE value. Now when using the model in production, if I use the complete data as training dataset, how to get the 7day lag value of the time period I'm planning to predict? I obviously won't have the target value, to calculate it's 7Day lag value. What to do now? To explain in more detail: So, I'm predicting sales amount (Target variable y). The model is trained on 20 predictors (X), and 7 of them are the lag value of the Target Variable, i.e. Sales Amount. The thing is, while preparing the model, I had access to both X & y dataset, thus I could easily calculate Y's Lag values. Now, when predicting for future timestamps, I won't be having y. So how do I calculate the lag values, which is required in the trained model's predictor columns now? submitted by /u/boredmonki [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07124 Abstract: Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, the so-called finetuning step. In contrast, aligning frozen LLMs without any extra data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guid…  ( 9 min )

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Hello everyone so I wanted to get into machinelearning and learn about ai Can someone help me with a roadmap, I would be really thankful submitted by /u/Fooda234 [link] [comments]  ( 8 min )

Hello, I'm currently preparing for oral exams in which I'll be evaluated on my understanding and proficiency in using Decision Trees, Random Forests, Neural Networks, and Support Vector Machines for various machine learning tasks (mostly spatial data). I'm contacting this community to gain valuable insights and guidance to excel in these exams. What are some crucial lessons you've learned in your machine-learning journey? Whether it's about model selection, data preprocessing, or debugging, I'm all ears for your experiences. What are some rules you have learned through practical work that are not so extensively described in classical literature? What are some mistakes that even professionals make when developing machine learning models? What are some common pitfalls to avoid when training neural networks? Thanks : submitted by /u/Aim_F0r_The_Moon [link] [comments]  ( 9 min )

Im trying to create a graph transformer-based model for de novo drug design (using graph transformer because I want to implement 3D data). I currently have 2 potential sources of primary data: PDBbind and CrossDocked2020. This would provide the protein-ligand structures. PDBbind is a more robust and higher quality dataset from what I know, and easier to work with. The problem is that it only contains about 20,000 complexes, and I'm not sure if that is enough for training a transformer. CrossDocked2020 contains millions of entries but I'm not sure about the quality and ease of use. Another dilemma is that I need/want to use a multi-task learning approach where the model is also being trained on bioactivity data, not just the structural information. This would require supplementation from sources like PubChem, ChEMBL, BDB, etc. and then I would need to align the data so it all matches up. If anyone can provide some guidance I'd really appreciate it. submitted by /u/Present_Network1959 [link] [comments]  ( 9 min )

I am supposed to be delivering a 5 minute presentation on tree-models as part of coursework requirement to a graduate class ON machine learning that I am enrolled in and I couldn't be more stressed! This is my first time 'learning' ML and I don't even know what I don't know about this topic!! If you were attending my presentation on tree models, what would you like to see (assuming this is a new topic for you too. but please provide feedback based on your [hopefully] extensive knowledge on this topic)? Here is what I have so far (based on google searches, some papers): -Introduction -Types of trees based on algo and techniques (basically classification trees and regression trees) -Then I am thinking of going off on a tangent about decision trees bec I have no clue about how to move this presentation forward -Real-world applications -Key takeaways (inserts clownface emoji) My prof asks the students questions about the topic as well. I am mostly concerned about WHAT to cover in 5 minutes without making look under-researched. Any redirection/suggestions will be appreciated! ​ submitted by /u/toomanymouthstofeed [link] [comments]  ( 9 min )

(I'm a Software Engineer who knows almost nothing about ML / NLP, so, apologies in advance if this doesn't make any sense.) I had a shower thought around if tokenization could incorporate meaning, so the models could be trained on "thoughts" instead of subword tokens (which is probably closer to how we humans think). To expand a little bit, we could first cluster all the words (maybe using the current day LLMs), then each token (or "thought") in the new scheme could represent a group of related words, followed by refinements that would be less and less important. For example, (cop_thought, cop_refinement) -> cop, but (cop_thought, police_refinement) -> police etc. So the encoding step would involve an LLM (which could possibly be relatively smaller), whose output would go to the actual model but the decoding would still be fairly straightforward. This could possibly free up some additional capacity in the actual model, assuming this heavy lifting tokenization makes its job easier, but as I'm typing it, I guess I'm simply moving some of the semantic understand happening in the hidden layers of the current day LLMs explicitly to the tokenization (encoding) step, which may not really change anything (if not make it worse). I'm still curious what folks think, if there's any related efforts (and all the ways I'm wrong -- https://meta.wikimedia.org/wiki/Cunningham%27s_Law). Thanks! submitted by /u/avamsi [link] [comments]  ( 9 min )

Keep in mind I'm very new to machine learning. I have an idea for a project where I train an AI to detect instances of someone being highly confident in a treatment for a condition, maybe biased toward more unconventional treatments. I was thinking that there may be many instances of people who posted that they were cured or nearly cured of such and such disease by an unconventional treatment on a forum about the condition. It may have been discussed for a little while, but ultimately buried in the pages of the forum. The plan would be to annotate instances I can find of such scenarios, maybe like this: [FIRST_PERSON_HIGH_CONFIDENCE_TREATMENT]I had severe [CONDITION]migraines[/CONDITION] for years, and nothing seemed to work. Then I tried [TREATMENT]grounding, where I walk barefoot on grass for 30 minutes every day[/TREATMENT]. I can [HIGH_CONFIDENCE]honestly say I've never felt better[/HIGH_CONFIDENCE]. My [CONDITION]migraines[/CONDITION] have reduced by 80%, and I'm [HIGH_CONFIDENCE]convinced this is a game-changer for me[/HIGH_CONFIDENCE].[/FIRST_PERSON_HIGH_CONFIDENCE_TREATMENT] Then train an AI with that data, so that it could detect cases of a person talking about themselves (to avoid hearsay) and saying, with high confidence, that a treatment worked for a condition. Then millions of forum posts could be fed to the AI to detect these, and the resulting data could be used to possibly discover effective treatments that are not in the mainstream. Any tips on getting started? I know almost nothing about this kind of stuff, like what models I should use, how to annotate it best (should I use relational labels?), whether to use a transformer or something else, stuff like that. Suggestions for books or other resources fit for a beginner that could help me learn how this could be done would be great too. submitted by /u/carbonflow45 [link] [comments]  ( 9 min )

I'm currently diving into the world of machine learning and transformers, and I'm trying to wrap my head around the concept of "attention" in transformer models. I've been reading papers and documentation, but I'm still struggling to fully grasp it. My Struggle: I get that attention involves multiplying "query" and "key" vectors to determine the importance of different words in a sequence, but I don't quite understand why this multiplication gives us a meaningful metric for importance. What I'm looking for: I'm comfortable with moderate level technicalities but require a deeper insight into the inner workings and rationale behind these mechanisms. Please share any insights, analogies, or technical details that can shed light on this concept. Thanks a bunch! submitted by /u/GraphicsMonster [link] [comments]  ( 9 min )

99.7% of its 8000 moves were legal with the longest game going 147 moves. You can test it here: https://github.com/adamkarvonen/chess_gpt_eval ​ https://preview.redd.it/821ydy7521qb1.png?width=1000&format=png&auto=webp&s=da6c96feaa527d0b7dfbf407bdc0210f3fcf947b More details here: https://twitter.com/a_karvonen/status/1705340535836221659 submitted by /u/seraine [link] [comments]  ( 9 min )

​ Hi everyone, I am working on a project that involves machine learning and computer vision. I want to train a model that can recognize and classify different types of signals used by the Indian railways. For this, I need a large and diverse image dataset of railway signals from various locations, angles, lighting conditions, etc. I have searched online for existing datasets, but I could not find any that suit my needs. So I wish to create my own dataset from scratch. However, I am not sure how to go about it. What are the best practices and tools for creating an image dataset? How do I collect, label, and organize the images? How do I ensure the quality and consistency of the data? submitted by /u/Responsible-Diver226 [link] [comments]  ( 9 min )

Hello, I am starting a ML course soon in college and I wanted to get a head start on the math part of things, since they keep saying the course is math heavy and hard. I know that it involves quite a lot of Linear Algebra, Calculus, and Probability and statistics, but what concepts in particular does ML focus on? If anyone has any Youtube or Udemy courses, as I have access to those, I would really appreciate it. For starters, I’m really aiming to just at least tackle the ML-specific math concepts. Thanks. submitted by /u/CrunchyMind [link] [comments]  ( 9 min )

TLDR: New training approach proposed to help smaller AI models to achieve state-of-the-art translation performance Large AI models like GPT-3 have good performance on translation tasks, but some smaller models struggle. Researchers from Johns Hopkins and Microsoft propose a new 2-stage fine-tuning method called ALMA that unlocks stronger translation abilities in smaller models with just 7-13 billion parameters. How it works: Fine-tune on monolingual data in non-English languages to improve comprehension Further fine-tune on small sets of high-quality human-translated parallel text The authors claim this achieves SOTA-level translation using far less data and compute than conventional methods: Matches performance of 175B parameter GPT-3 and 54B parameter NLLB with only 7-13B parameters Reaches NLLB-level quality with just 1 billion monolingual tokens and 18 hours of training I think this shows that smaller models can reach SOTA translation with specialized fine-tuning, so we may not need endlessly bigger datasets and models to get better performance. Looks like deliberate tuning targeting key language skills could be more important. Full summary here. Paper (preprint) is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I've been reading the HyperDreamBooth paper and am confused about the number of parameters and corresponding matrix shapes in section 4.1 and figure 3 (below). ​ Figure 3 from the paper Maybe there's something more than just matrix multiplication, because the numbers don't add up. If there are two matrices, A & B, of size n,r and r,m, and r = 1, and you multiply them, then it implies that n + m = 386k, which seems to be a bit much for a number of parameters in a single layer. Then we have two matrices of size a,r and r,b, where a = 100, b = 50 and there are 28k variables, according to the figure - in 4.1 they say 30k. If there's 30k, that would imply r = 200, for matrices of shape 100,200 and 200,50. I guess 386k and 28k are for the whole models, n and m differ from layer to layer, and r stays at one. Quite surprising to me that approximating a n,m shaped matrix with a product of n,1 and 1,m vectors would work. Even more surprising that apparently you could squeeze it further to 100,1 and 50,1. submitted by /u/Foxtr0t [link] [comments]  ( 9 min )

Hey everyone! Multi-part question. I have scraped text (I repeat, text, and not structured data such as tables or something) from a medical site, and I want to know how to clean it. And when I say clean, I don't mean removal of html tags and such. I already have the paragraphs in plain text but there is a lot of spammy stuff like "You are not signed in; subscribe to this newsletter; by checking this box, I agree to the terms and conditions, etc." This text is not the exact same in all the paragraphs but there is high similarity. I would have thought there would be many tools to clean text and remove unrelated chunks like these but all I have been able to find has to do with cleaning html tags, changing date-time format and so on. Am I missing something or is this actually difficult? Secondly, the spammy text I mentioned is from just one site. I will be eventually scaling to many sites and god knows what random text I'll have to clean then. I used OpenAI embeddings and cosine similarity on the medical text to find similar paragraphs. The results were not great. Is there a way to improve the similarity search? I will be trying FAISS next but wanted to know what else I can do. It was suggested to me to use a pretrained embedding model specific to medical data. However, I found only one such model which is 20 gigs! I'm just getting started with these, so, appreciate any help I can get. Thanks a ton! submitted by /u/yipra97 [link] [comments]  ( 9 min )

Hey folks - I have been tasked with a project at work that is outside of my typical realm (non technical background), and I would love any and all insight on it! I have been asked to compare the top ten llms for research before we implement an llm for our company. So far my research has felt scattered because I’m not what directions to go in. I’ve been looking at things like open source vs closed source, parameters, tokens, what the license looks like (available for commercial use), and pricing. If anyone has thoughts on resources to look at or better ways to approach this, I would really appreciate it! submitted by /u/Greatvalueaidybryant [link] [comments]  ( 9 min )

Relatively novice ML practitioner here. My research is on various medical image segmentation problems, including brain 3D US (glioma), lung CT (interstitial lung disease in scleroderma patients), etc. using the PyTorch ecosystem (probably including frameworks such as MONAI) I'll have to conduct several experiments on various model architectures on parameters in the coming months. Specifically, these are what I'm gonna need: Experiment tracking (model architecture, training configuration, hyperparameters, evaluation metrics) Model storage (would be nice if there's a better way to store my model's parameters other than storing tons of .pth file on my harddisk or google drive) (Optional) Visualization (sample predictions of the model on the training or validation sets, maybe every 20 epochs or sth) Would like to hear any suggestions from the community I've found wandb, clearML, neptune, and Aim; but trying each of them individually would be too time-consuming considering my current schedule. Thanks in advance! submitted by /u/mimivirus2 [link] [comments]  ( 9 min )

submitted by /u/jarekduda [link] [comments]  ( 8 min )

I found an interesting arxiv paper mentioning that some optimizers can occur numerical instability for training neural network. Link: https://arxiv.org/abs/2307.16189 This can be a simple approach for low-precision neural network with 16-bit and future 8-bit or 4-bit. submitted by /u/Klutzy_Divide3485 [link] [comments]  ( 9 min )

I like generating long LaTeX documents from org-mode because, for one thing, org-mode has nice section folding. But not everyone I work with uses Emacs, so its better to work in LaTeX directly rather than have Emacs generate LaTeX. AUCTeX has section folding for LaTeX documents, though so far I’ve only has limited success at […] Navigating a LaTeX file first appeared on John D. Cook.  ( 5 min )

It’s surprisingly hard to find a complete list of HTML entities in the form of a data file. There are numerous sites that give lists, often incomplete, in a page formatted to be human-readable but not machine-readable. Here’s an XML file from the W3C. Here’s a two-column text file I created from the W3C data. HTML entity data first appeared on John D. Cook.  ( 5 min )

I’ve needed to use double-struck capital letters lately, also called blackboard bold. There are a few quirks in how they are represented in Unicode and in HTML entities, so I’m leaving some notes for myself here and for anyone else who might need to look this up. Unicode The double-struck capital letters are split into […] Double-struck capital letters first appeared on John D. Cook.  ( 6 min )

“Sometimes, you don’t know where you’re going until you get there.” – Schmarzo-ism? Yes, writing this blog turned into a journey. I started in one direction, but after several twists and turns, I ended up with this concept – that use case-centric language models can be combined into entity-centric language models that can support multiple… Read More »Use Case Language Models: Taming the LLM Beast – Part 1 The post Use Case Language Models: Taming the LLM Beast – Part 1 appeared first on Data Science Central.  ( 22 min )

Hi has anyone ever attempted to do RL with comsol multiphysics or any other FEM based simulation tool? submitted by /u/Practical_Ad_8782 [link] [comments]  ( 8 min )

Hi all, I'd like to share some of what I've learned over the last ~year getting up to speed with Python and RL. Mini RL Lab is a setup and workflow that works well for me to debug and experiment with concepts like agent algorithms, world models, planning, plasticity, transformers etc, and other beginners might find it a useful starting point for their own experiments. Link: https://github.com/modelbased/minirllab submitted by /u/thiagoazevedo [link] [comments]  ( 9 min )

As I understand, like in the example rock paper scissors, they compute the regret based on the last state. or in poker, they compute regret based on what last happened. But is it possible to compute regret for two or more moves ago? like "i wish when I saw 10 moves ago, I did x"? or has it been possible from the start and I just understood counterfactual regret minimization wrongly? submitted by /u/oniongarlic88 [link] [comments]  ( 9 min )

Hey everyone, I've been taking a look at hypernetworks and noticed they've been used in some cool ways in RL with stuff like Qmix and this Metalearning paper. Quick recap: f is our regular neural network: takes in input x and outputs y using weights \theta. g is the hypernetwork, it creates the weights \theta for f based on its own weights \phi and maybe the input x. Original paper: https://arxiv.org/abs/1609.09106 I watched this talk and it hinted that hypernetworks might even be better than our regular networks even for supervised learning regular tasks. So, I'm curious: What's the deal with hypernetworks? What makes them good or not vs regular neural networks? I get that they're good for metalearning, but could they also be a game-changer for other things, like sample efficiency? Does anyone have thoughts or reads on this? Thanks! ​ ​ submitted by /u/LazyButAmbitious [link] [comments]  ( 9 min )

DON'T KNOW IF THIS POST BELONGS HERE BUT... I have zero knowledge about the AI/ML. And the thing is my college is asking us to do projects on Deep learning. They specifically asked us to pick a base paper from ACM Journals or IEEE Transactions which has been published after 2020. And implement these papers and do some novelty work. And I have zero clue how to proceed. submitted by /u/um2_doma [link] [comments]  ( 9 min )

A fundamental shortcoming of the concept of Nash equilibrium is its computational intractability: approximating Nash equilibria in normal-form games is PPAD-hard. In this paper, inspired by the ideas of smoothed analysis, we introduce a relaxed variant of Nash equilibrium called $\sigma$-smooth Nash equilibrium, for a smoothness parameter $\sigma$. In a $\sigma$-smooth Nash equilibrium, players only need to achieve utility at least as high as their best deviation to a $\sigma$-smooth strategy, which is a distribution that does not put too much mass (as parametrized by $\sigma$) on any fixed action. We distinguish two variants of $\sigma$-smooth Nash equilibria: strong $\sigma$-smooth Nash equilibria, in which players are required to play $\sigma$-smooth strategies under equilibrium play, and weak $\sigma$-smooth Nash equilibria, where there is no such requirement. We show that both weak and strong $\sigma$-smooth Nash equilibria have superior computational properties to Nash equilibria: when $\sigma$ as well as an approximation parameter $\epsilon$ and the number of players are all constants, there is a constant-time randomized algorithm to find a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in normal-form games. In the same parameter regime, there is a polynomial-time deterministic algorithm to find a strong $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in a normal-form game. These results stand in contrast to the optimal algorithm for computing $\epsilon$-approximate Nash equilibria, which cannot run in faster than quasipolynomial-time. We complement our upper bounds by showing that when either $\sigma$ or $\epsilon$ is an inverse polynomial, finding a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibria becomes computationally intractable.  ( 3 min )

Pneumonia is the leading infectious cause of infant death in the world. When identified early, it is possible to alter the prognosis of the patient, one could use imaging exams to help in the diagnostic confirmation. Performing and interpreting the exams as soon as possible is vital for a good treatment, with the most common exam for this pathology being chest X-ray. The objective of this study was to develop a software that identify the presence or absence of pneumonia in chest radiographs. The software was developed as a computational model based on machine learning using transfer learning technique. For the training process, images were collected from a database available online with children's chest X-rays images taken at a hospital in China. After training, the model was then exposed to new images, achieving relevant results on identifying such pathology, reaching 98% sensitivity and 97.3% specificity for the sample used for testing. It can be concluded that it is possible to develop a software that identifies pneumonia in chest X-ray images.  ( 2 min )

3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to decompose complex natural language queries into semantic constituents and employs a visual grounding tool, such as OpenScene or LERF, to identify objects in a 3D scene. The LLM then evaluates the spatial and commonsense relations among the proposed objects to make a final grounding decision. Our method does not require any labeled training data and can generalize to novel 3D scenes and arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and demonstrate state-of-the-art zero-shot grounding accuracy. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries, making LLM-Grounder an effective approach for 3D vision-language tasks in robotics. Videos and interactive demos can be found on the project website https://chat-with-nerf.github.io/ .  ( 2 min )

Adversarial examples, deliberately crafted using small perturbations to fool deep neural networks, were first studied in image processing and more recently in NLP. While approaches to detecting adversarial examples in NLP have largely relied on search over input perturbations, image processing has seen a range of techniques that aim to characterise adversarial subspaces over the learned representations. In this paper, we adapt two such approaches to NLP, one based on nearest neighbors and influence functions and one on Mahalanobis distances. The former in particular produces a state-of-the-art detector when compared against several strong baselines; moreover, the novel use of influence functions provides insight into how the nature of adversarial example subspaces in NLP relate to those in image processing, and also how they differ depending on the kind of NLP task.  ( 2 min )

Bayesian Optimization (BO) is an effective approach for global optimization of black-box functions when function evaluations are expensive. Most prior works use Gaussian processes to model the black-box function, however, the use of kernels in Gaussian processes leads to two problems: first, the kernel-based methods scale poorly with the number of data points and second, kernel methods are usually not effective on complex structured high dimensional data due to curse of dimensionality. Therefore, we propose a novel black-box optimization algorithm where the black-box function is modeled using a neural network. Our algorithm does not need a Bayesian neural network to estimate predictive uncertainty and is therefore computationally favorable. We analyze the theoretical behavior of our algorithm in terms of regret bound using advances in NTK theory showing its efficient convergence. We perform experiments with both synthetic and real-world optimization tasks and show that our algorithm is more sample efficient compared to existing methods.  ( 2 min )

Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the difficulty of capturing the developer intent. In this work, we address these challenges by endowing pre-trained large language models (LLMs) of code with the knowledge of prior, relevant edits. The generative capability of the LLMs helps address the diversity in code changes and conditioning code generation on prior edits helps capture the latent developer intent. We evaluate two well-known LLMs, Codex and CodeT5, in zero-shot and fine-tuning settings respectively. In our experiments with two datasets, the knowledge of prior edits boosts the performance of the LLMs significantly and enables them to generate 29% and 54% more correctly edited code in top-1 suggestions relative to the current state-of-the-art symbolic and neural approaches, respectively.  ( 2 min )

Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples are available at https://comospeech.github.io/.  ( 3 min )

Machine learning models trained with structural health monitoring data have become a powerful tool for system identification. This paper presents a physics-informed Gaussian process (GP) model for Timoshenko beam elements. The model is constructed as a multi-output GP with covariance and cross-covariance kernels analytically derived based on the differential equations for deflections, rotations, strains, bending moments, shear forces and applied loads. Stiffness identification is performed in a Bayesian format by maximising a posterior model through a Markov chain Monte Carlo method, yielding a stochastic model for the structural parameters. The optimised GP model is further employed for probabilistic predictions of unobserved responses. Additionally, an entropy-based method for physics-informed sensor placement optimisation is presented, exploiting heterogeneous sensor position information and structural boundary conditions built into the GP model. Results demonstrate that the proposed approach is effective at identifying structural parameters and is capable of fusing data from heterogeneous and multi-fidelity sensors. Probabilistic predictions of structural responses and internal forces are in closer agreement with measured data. We validate our model with an experimental setup and discuss the quality and uncertainty of the obtained results. The proposed approach has potential applications in the field of structural health monitoring (SHM) for both mechanical and structural systems.  ( 3 min )

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.  ( 3 min )

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model's efficacy, achieving an impressive 97\% accuracy and 97\% F1 score in predicting DVT, and an outstanding 98.3\% accuracy and 98.4\% F1 score in predicting PE. These findings emphasize the model's robustness and its potential to significantly contribute to VTE research.  ( 2 min )

Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age. Even though the state-of-the-art models successfully modify the requested attributes, they simultaneously modify other important characteristics of the image, such as a person's identity. In this paper, we focus on solving this problem by introducing PluGeN4Faces, a plugin to StyleGAN, which explicitly disentangles face attributes from a person's identity. Our key idea is to perform training on images retrieved from movie frames, where a given person appears in various poses and with different attributes. By applying a type of contrastive loss, we encourage the model to group images of the same person in similar regions of latent space. Our experiments demonstrate that the modifications of face attributes performed by PluGeN4Faces are significantly less invasive on the remaining characteristics of the image than in the existing state-of-the-art models.  ( 2 min )

We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such as human activity analysis where observed accelerometer time series contains segments representing distinct activities, which we call pure states, as well as periods characterized by continuous transition among these pure states. To capture this transitory behavior, the dynamical Wasserstein barycenter (DWB) model of Cheng et al. in 2021 [1] associates with each pure state a data-generating distribution and models the continuous transitions among these states as a Wasserstein barycenter of these distributions with dynamically evolving weights. Focusing on the univariate case where Wasserstein distances and barycenters can be computed in closed form, we extend [1] specifically relaxing the parameterization of the pure states as Gaussian distributions. We highlight issues related to the uniqueness in identifying the model parameters as well as uncertainties induced when estimating a dynamically evolving distribution from a limited number of samples. To ameliorate non-uniqueness, we introduce regularization that imposes temporal smoothness on the dynamics of the barycentric weights. A quantile-based approximation of the pure state distributions yields a finite dimensional estimation problem which we numerically solve using cyclic descent alternating between updates to the pure-state quantile functions and the barycentric weights. We demonstrate the utility of the proposed algorithm in segmenting both simulated and real world human activity time series.  ( 3 min )

This work introduces Physics-informed State-space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transport-dominated systems such as chemical, biomedical, and power plants. Traditional data-driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics-informing using components' Partial Differential Equations (PDEs), resulting in a physics-constrained, end-to-end differentiable forward dynamics model. Through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than purely data-driven models. Beyond accuracy, there are several compelling use cases for PSMs. In this work, we showcase two: the creation of a nonlinear supervisory controller through a sequentially updated state-space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates the ability of PSMs to handle both constant and time-dependent constraints, while the latter illustrates their value in system diagnostics and fault detection. We further posit that PSMs could serve as a foundation for Digital Twins, constantly updated digital representations of physical systems.  ( 2 min )

This study introduces a novel hierarchical divisive clustering approach with stochastic splitting functions (SSFs) to enhance classification performance in multi-class datasets through hierarchical classification (HC). The method has the unique capability of generating hierarchy without requiring explicit information, making it suitable for datasets lacking prior knowledge of hierarchy. By systematically dividing classes into two subsets based on their discriminability according to the classifier, the proposed approach constructs a binary tree representation of hierarchical classes. The approach is evaluated on 46 multi-class time series datasets using popular classifiers (svm and rocket) and SSFs (potr, srtr, and lsoo). The results reveal that the approach significantly improves classification performance in approximately half and a third of the datasets when using rocket and svm as the classifier, respectively. The study also explores the relationship between dataset features and HC performance. While the number of classes and flat classification (FC) score show consistent significance, variations are observed with different splitting functions. Overall, the proposed approach presents a promising strategy for enhancing classification by generating hierarchical structure in multi-class time series datasets. Future research directions involve exploring different splitting functions, classifiers, and hierarchy structures, as well as applying the approach to diverse domains beyond time series data. The source code is made openly available to facilitate reproducibility and further exploration of the method.  ( 2 min )

To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planning abstract subgoal sequences, visually reasoning about the underlying plans, and executing actions in accordance with the devised plan through visual-motor control. We propose Compositional Foundation Models for Hierarchical Planning (HiP), a foundation model which leverages multiple expert foundation model trained on language, vision and action data individually jointly together to solve long-horizon tasks. We use a large language model to construct symbolic plans that are grounded in the environment through a large video diffusion model. Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos. To enable effective reasoning within this hierarchy, we enforce consistency between the models via iterative refinement. We illustrate the efficacy and adaptability of our approach in three different long-horizon table-top manipulation tasks.  ( 2 min )

Session-based recommendation, aiming at making the prediction of the user's next item click based on the information in a single session only even in the presence of some random user's behavior, is a complex problem. This complex problem requires a high-capability model of predicting the user's next action. Most (if not all) existing models follow the encoder-predictor paradigm where all studies focus on how to optimize the encoder module extensively in the paradigm but they ignore how to optimize the predictor module. In this paper, we discover the existing critical issue of the low-capability predictor module among existing models. Motivated by this, we propose a novel framework called \emph{\underline{S}ession-based \underline{R}ecommendation with \underline{Pred}ictor \underline{A}dd-\underline{O}n} (SR-PredictAO). In this framework, we propose a high-capability predictor module which could alleviate the effect of random user's behavior for prediction. It is worth mentioning that this framework could be applied to any existing models, which could give opportunities for further optimizing the framework. Extensive experiments on two real benchmark datasets for three state-of-the-art models show that \emph{SR-PredictAO} out-performs the current state-of-the-art model by up to 2.9\% in HR@20 and 2.3\% in MRR@20. More importantly, the improvement is consistent across almost all the existing models on all datasets, which could be regarded as a significant contribution in the field.  ( 2 min )

Action scene understanding in soccer is a challenging task due to the complex and dynamic nature of the game, as well as the interactions between players. This article provides a comprehensive overview of this task divided into action recognition, spotting, and spatio-temporal action localization, with a particular emphasis on the modalities used and multimodal methods. We explore the publicly available data sources and metrics used to evaluate models' performance. The article reviews recent state-of-the-art methods that leverage deep learning techniques and traditional methods. We focus on multimodal methods, which integrate information from multiple sources, such as video and audio data, and also those that represent one source in various ways. The advantages and limitations of methods are discussed, along with their potential for improving the accuracy and robustness of models. Finally, the article highlights some of the open research questions and future directions in the field of soccer action recognition, including the potential for multimodal methods to advance this field. Overall, this survey provides a valuable resource for researchers interested in the field of action scene understanding in soccer.  ( 2 min )

The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related to identity swapping or expression transferring are still overlooked. And the forgery traces hidden in unknown attacks from the open-world unlabeled faces still remain under-explored. To push the related frontier research, we introduce a new benchmark called Open-World DeepFake Attribution (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios. Meanwhile, we propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set. In addition, we extend the CPL framework with a multi-stage paradigm that leverages pre-train technique and iterative learning to further enhance traceability performance. Extensive experiments verify the superiority of our proposed method on the OW-DFA and also demonstrate the interpretability of deepfake attribution task and its impact on improving the security of deepfake detection area.  ( 2 min )

Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For many years this bottleneck has persisted, as many thought sequential models could not be parallelized. We challenge this long-held belief with our parallel algorithm that accelerates GPU evaluation of sequential models by up to 3 orders of magnitude faster without compromising output accuracy. The algorithm does not need any special structure in the sequential models' architecture, making it applicable to a wide range of architectures. Using our method, training sequential models can be more than 10 times faster than the common sequential method without any meaningful difference in the training results. Leveraging this accelerated training, we discovered the efficacy of the Gated Recurrent Unit in a long time series classification problem with 17k time samples. By overcoming the training bottleneck, our work serves as the first step to unlock the potential of non-linear sequential models for long sequence problems.  ( 2 min )

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.  ( 2 min )

Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these local optima to improve model performance remains a challenging task. Given the inherent complexity of neural networks, the simple arithmetic averaging of the obtained local optima models in undesirable results. This paper proposes a {\em soft merging} method that facilitates rapid merging of multiple models, simplifies the merging of specific parts of neural networks, and enhances robustness against malicious models with extreme values. This is achieved by learning gate parameters through a surrogate of the $l_0$ norm using hard concrete distribution without modifying the model weights of the given local optima models. This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent. Thorough experiments underscore the effectiveness and superior performance of the merged neural networks.

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse problems. Synthetically generated images are utilized for data augmentation and training a Vision Transformer model. The classification performance of the model is evaluated using accuracy, recall, and precision scores. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as evidenced by the superior diversity scores and classification scores.

This paper presents our solution for the Real Robot Challenge (RRC) III, a competition featured in the NeurIPS 2022 Competition Track, aimed at addressing dexterous robotic manipulation tasks through learning from pre-collected offline data. Participants were provided with two types of datasets for each task: expert and mixed datasets with varying skill levels. While the simplest offline policy learning algorithm, Behavioral Cloning (BC), performed remarkably well when trained on expert datasets, it outperformed even the most advanced offline reinforcement learning (RL) algorithms. However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory. Upon examining the mixed datasets, we observed that they contained a significant amount of expert data, although this data was unlabeled. To address this issue, we proposed a semi-supervised learning-based classifier to identify the underlying expert behavior within mixed datasets, effectively isolating the expert data. To further enhance BC's performance, we leveraged the geometric symmetry of the RRC arena to augment the training dataset through mathematical transformations. In the end, our submission surpassed that of all other participants, even those who employed complex offline RL algorithms and intricate data processing and feature engineering techniques.

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.

Numerically solving partial differential equations typically requires fine discretization to resolve necessary spatiotemporal scales, which can be computationally expensive. Recent advances in deep learning have provided a new approach to solving partial differential equations that involves the use of neural operators. Neural operators are neural network architectures that learn mappings between function spaces and have the capability to solve partial differential equations based on data. This study utilizes a novel neural operator called Hyena, which employs a long convolutional filter that is parameterized by a multilayer perceptron. The Hyena operator is an operation that enjoys sub-quadratic complexity and state space model to parameterize long convolution that enjoys a global receptive field. This mechanism enhances the model's comprehension of the input's context and enables data-dependent weight for different partial differential equations instances. To measure how effective the layers are in solving partial differential equations, we conduct experiments on Diffusion-Reaction equation and Navier Stokes equation. Our findings indicate Hyena Neural operator can serve as an efficient and accurate model for learning partial differential equations solution operator. The data and code used can be found at: https://github.com/Saupatil07/Hyena-Neural-Operator  ( 2 min )

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data. However, adversaries can still infer individual information through inference attacks (e.g. differential attacks) on these training parameters. As a result, Differential Privacy (DP) has been widely used in FL to prevent such attacks. We consider differentially private federated learning in a resource-constrained scenario, where both privacy budget and communication round are constrained. By theoretically analyzing the convergence, we can find the optimal number of differentially private local iterations for clients between any two sequential global updates. Based on this, we design an algorithm of differentially private federated learning with adaptive local iterations (ALI-DPFL). We experiment our algorithm on the FashionMNIST and CIFAR10 datasets, and demonstrate significantly better performances than previous work in the resource-constraint scenario.

Serial femtosecond crystallography at X-ray free electron laser facilities opens a new era for the determination of crystal structure. However, the data processing of those experiments is facing unprecedented challenge, because the total number of diffraction patterns needed to determinate a high-resolution structure is huge. Machine learning methods are very likely to play important roles in dealing with such a large volume of data. Convolutional neural networks have made a great success in the field of pattern classification, however, training of the networks need very large datasets with labels. Th is heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns. In this article we present our job on the classification of diffraction pattern by weakly supervised algorithms, with the aim of reducing as much as possible the size of the labeled dataset required for training. Our result shows that weakly supervised methods can significantly reduce the need for the number of labeled patterns while achieving comparable accuracy to fully supervised methods.

With the advent of the IoT, AI and ML/DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. Consequently, the realm of data-driven medical applications has garnered significant attention spanning academia and industry, ushering in marked enhancements in healthcare delivery quality. Despite these strides, the adoption of AI-driven medical applications remains hindered by formidable challenges, including the arduous task of meeting security, privacy, and quality of service (QoS) standards. Recent developments in federated learning have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. We delve into the contemporary research trends and their outcomes, unravelling the intricacies of designing reliable and scalable FL models. Our survey outlines the foundational statistical predicaments of FL, confronts device-related obstacles, delves into security challenges, and navigates the intricate terrain of privacy concerns, all while spotlighting its transformative potential within the medical domain. A primary focus of our study rests on medical applications, where we underscore the weighty burden of global cancer and illuminate the potency of FL in engendering computer-aided diagnosis tools that address this challenge with heightened efficacy.

Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feature monitoring and verification in recommendation use cases. Drifter addresses limitations of existing methods by delivering agile, responsive, and adaptable data quality monitoring, enabling real-time root cause analysis, drift detection and insights into problematic production events. Integrating state-of-the-art online feature ranking for sparse data and anomaly detection ideas, Drifter is highly scalable and resource-efficient, requiring only two threads and less than a gigabyte of RAM per production deployments that handle millions of instances per minute. Evaluation on real-world data sets demonstrates Drifter's effectiveness in alerting and mitigating data quality issues, substantially improving reliability and performance of real-time live recommender systems.

Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demonstrations. Therefore, we propose a novel iterative learning algorithm for identifying expert trajectories in unlabeled mixed-quality robotics datasets given a minimal set of positive examples, surpassing existing algorithms in terms of accuracy. We show that applying behavioral cloning to the resulting filtered dataset outperforms several competitive offline reinforcement learning and imitation learning baselines. We perform experiments on a range of simulated locomotion tasks and on two challenging manipulation tasks on a real robotic system; in these experiments, our method showcases state-of-the-art performance. Our website: \url{https://sites.google.com/view/offline-policy-learning-pubc}.

Connectionist temporal classification (CTC) is commonly adopted for sequence modeling tasks like speech recognition, where it is necessary to preserve order between the input and target sequences. However, CTC is only applied to deterministic sequence models, where the latent space is discontinuous and sparse, which in turn makes them less capable of handling data variability when compared to variational models. In this paper, we integrate CTC with a variational model and derive loss functions that can be used to train more generalizable sequence models that preserve order. Specifically, we derive two versions of the novel variational CTC based on two reasonable assumptions, the first being that the variational latent variables at each time step are conditionally independent; and the second being that these latent variables are Markovian. We show that both loss functions allow direct optimization of the variational lower bound for the model log-likelihood, and present computationally tractable forms for implementing them.

We present a neural network for mitigating pseudorange bias to improve localization performance with data collected from Android smartphones. We represent pseudorange bias using a pragmatic satellite-wise Multiple Layer Perceptron (MLP), the inputs of which are six satellite-receiver-context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To supervise the training process, we carefully calculate the target values of pseudorange bias using location ground truth and smoothing techniques and optimize a loss function containing the estimation residuals of smartphone clock bias. During the inference process, we employ model-based localization engines to compute locations with pseudoranges corrected by the neural network. Consequently, this hybrid pipeline can attend to both pseudorange bias and noise. We evaluate the framework on an open dataset and consider four application scenarios for investigating fingerprinting and cross-trace localization in rural and urban areas. Extensive experiments demonstrate that the proposed framework outperforms model-based and state-of-the-art data-driven approaches.

This work presents a deep learning surrogate model for the fast simulation of high-dimensional frequency selective surfaces. We consider unit-cells which are built as multiple concatenated stacks of screens and their design requires the control over many geometrical degrees of freedom. Thanks to the introduction of physical insight into the model, it can produce accurate predictions of the S-parameters of a certain structure after training with a reduced dataset.The proposed model is highly versatile and it can be used with any kind of frequency selective surface, based on either perforations or patches of any arbitrary geometry. Numeric examples are presented here for the case of frequency selective surfaces composed of screens with rectangular perforations, showing an excellent agreement between the predicted performance and such obtained with a full-wave simulator.

The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a labor-intensive human effort. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. This paper introduces a new method called Domain-knowledge Inspired Pseudo Supervision (DIPS) which utilizes domain-informed Gaussian Mixture Models to generate pseudo annotations to enable the use of traditional supervised metrics. This method was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which were designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. DIPS proves its effectiveness by outperforming various GAN evaluation metrics, including FID, when selecting the optimal saved checkpoint model. It is also evaluated against truly supervised metrics. Furthermore, DIPS showcases its robustness and interpretability by demonstrating a strong correlation with truly supervised metrics, highlighting its superiority over existing state-of-the-art alternatives. The code and data to replicate the results can be found on the official Github repository: https://github.com/Hindawi91/DIPS

Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dynamic behaviors introduce new challenges to the system software in an ML system since the overall system load is not completely predictable, unlike traditional ML workloads. In addition, RTMM workloads require real-time processing, involve highly heterogeneous models, and target resource-constrained devices. Under such circumstances, developing an effective scheduler gains more importance to better utilize underlying hardware considering the unique characteristics of RTMM workloads. Therefore, we propose a new scheduler, DREAM, which effectively handles various dynamicity in RTMM workloads targeting multi-accelerator systems. DREAM quantifies the unique requirements for RTMM workloads and utilizes the quantified scores to drive scheduling decisions, considering the current system load and other inference jobs on different models and input frames. DREAM utilizes tunable parameters that provide fast and effective adaptivity to dynamic workload changes. In our evaluation of five scenarios of RTMM workload, DREAM reduces the overall UXCost, which is an equivalent metric of the energy-delay product (EDP) for RTMM defined in the paper, by 32.2% and 50.0% in the geometric mean (up to 80.8% and 97.6%) compared to state-of-the-art baselines, which shows the efficacy of our scheduling methodology.

We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing scalability and the utilization of sparse representations. Many algorithms in CLRS require global memory or information exchange, mirrored in its execution model, which constructs fully connected (not sparse) graphs based on the underlying problem. Despite CLRS's aim of assessing how effectively learned algorithms can generalize to larger instances, the existing execution model becomes a significant constraint due to its demanding memory requirements and runtime (hard to scale). However, many important algorithms do not demand a fully connected graph; these algorithms, primarily distributed in nature, align closely with the message-passing paradigm employed by Graph Neural Networks. Hence, we propose SALSA-CLRS, an extension of the current CLRS benchmark specifically with scalability and sparseness in mind. Our approach includes adapted algorithms from the original CLRS benchmark and introduces new problems from distributed and randomized algorithms. Moreover, we perform a thorough empirical evaluation of our benchmark. Code is publicly available at https://github.com/jkminder/SALSA-CLRS.

This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.

The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. Diffractive optical networks provide a promising solution for implementing artificial intelligence with high-speed and low-power consumption. Most of the reported diffractive optical networks focus on single or multiple tasks that do not involve environmental interaction, such as object recognition and image classification. In contrast, the networks capable of performing decision-making and control have not yet been developed to our knowledge. Here, we propose using deep reinforcement learning to implement diffractive optical networks that imitate human-level decision-making and control capability. Such networks taking advantage of a residual architecture, allow for finding optimal control policies through interaction with the environment and can be readily implemented with existing optical devices. The superior performance of these networks is verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros., and Car Racing. Finally, we present an experimental demonstration of playing Tic-Tac-Toe by leveraging diffractive optical networks based on a spatial light modulator. Our work represents a solid step forward in advancing diffractive optical networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence. It may find exciting applications in autonomous driving, intelligent robots, and intelligent manufacturing.

In many applications in data clustering, it is desirable to find not just a single partition into clusters but a sequence of partitions describing the data at different scales, or levels of coarseness. A natural problem then is to analyse and compare the (not necessarily hierarchical) sequences of partitions that underpin such multiscale descriptions of data. Here, we introduce a filtration of abstract simplicial complexes, denoted the Multiscale Clustering Filtration (MCF), which encodes arbitrary patterns of cluster assignments across scales, and we prove that the MCF produces stable persistence diagrams. We then show that the zero-dimensional persistent homology of the MCF measures the degree of hierarchy in the sequence of partitions, and that the higher-dimensional persistent homology tracks the emergence and resolution of conflicts between cluster assignments across the sequence of partitions. To broaden the theoretical foundations of the MCF, we also provide an equivalent construction via a nerve complex filtration, and we show that in the hierarchical case, the MCF reduces to a Vietoris-Rips filtration of an ultrametric space. We briefly illustrate how the MCF can serve to characterise multiscale clustering structures in numerical experiments on synthetic data.

Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions toward more liveable, equitable, and sustainable cities.

We tackle the problem of robust novelty detection, where we aim to detect novelties in terms of semantic content while being invariant to changes in other, irrelevant factors. Specifically, we operate in a setup with multiple environments, where we determine the set of features that are associated more with the environments, rather than to the content relevant for the task. Thus, we propose a method that starts with a pretrained embedding and a multi-env setup and manages to rank the features based on their environment-focus. First, we compute a per-feature score based on the feature distribution variance between envs. Next, we show that by dropping the highly scored ones, we manage to remove spurious correlations and improve the overall performance by up to 6%, both in covariance and sub-population shift cases, both for a real and a synthetic benchmark, that we introduce for this task.

With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However, there still exists a difficulty in modeling discrete variables (columns) of tabular data. In this work, we propose to process continuous and discrete variables separately (but being conditioned on each other) by two diffusion models. The two diffusion models are co-evolved during training by reading conditions from each other. In order to further bind the diffusion models, moreover, we introduce a contrastive learning method with a negative sampling method. In our experiments with 11 real-world tabular datasets and 8 baseline methods, we prove the efficacy of the proposed method, called CoDi.

Modern deep networks are trained with stochastic gradient descent (SGD) whose key parameters are the number of data considered at each step or batch size $B$, and the step size or learning rate $\eta$. For small $B$ and large $\eta$, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the `temperature' $T\equiv \eta/B$. Yet this description is observed to break down for sufficiently large batches $B\geq B^*$, or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here we resolve these questions for a teacher-student perceptron classification model, and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the $B$-$\eta$ plane that separates three dynamical phases: $\textit{(i)}$ a noise-dominated SGD governed by temperature, $\textit{(ii)}$ a large-first-step-dominated SGD and $\textit{(iii)}$ GD. These different phases also corresponds to different regimes of generalization error. Remarkably, our analysis reveals that the batch size $B^*$ separating regimes $\textit{(i)}$ and $\textit{(ii)}$ scale with the size $P$ of the training set, with an exponent that characterizes the hardness of the classification problem.

A variety of different performance metrics are commonly used in the machine learning literature for the evaluation of classification systems. Some of the most common ones for measuring quality of hard decisions are standard and balanced accuracy, standard and balanced error rate, F-beta score, and Matthews correlation coefficient (MCC). In this document, we review the definition of these and other metrics and compare them with the expected cost (EC), a metric introduced in every statistical learning course but rarely used in the machine learning literature. We show that both the standard and balanced error rates are special cases of the EC. Further, we show its relation with F-beta score and MCC and argue that EC is superior to these traditional metrics for being based on first principles from statistics, and for being more general, interpretable, and adaptable to any application scenario. The metrics mentioned above measure the quality of hard decisions. Yet, most modern classification systems output continuous scores for the classes which we may want to evaluate directly. Metrics for measuring the quality of system scores include the area under the ROC curve, equal error rate, cross-entropy, Brier score, and Bayes EC or Bayes risk, among others. The last three metrics are special cases of a family of metrics given by the expected value of proper scoring rules (PSRs). We review the theory behind these metrics, showing that they are a principled way to measure the quality of the posterior probabilities produced by a system. Finally, we show how to use these metrics to compute a system's calibration loss and compare this metric with the widely-used expected calibration error (ECE), arguing that calibration loss based on PSRs is superior to the ECE for being more interpretable, more general, and directly applicable to the multi-class case, among other reasons.

In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is the substantial learning gap between seen and novel categories, as the model learns the former faster due to accurate supervisory information. To address this, we introduce 1) an adaptive margin loss based on estimated class distribution, which encourages a large negative margin for samples in seen classes, to synchronize learning paces, and 2) pseudo-label contrastive clustering, which pulls together samples which are likely from the same class in the output space, to enhance novel class discovery. Our extensive evaluations on multiple datasets demonstrate that existing models still hinder novel class learning, whereas our approach strikingly balances both seen and novel classes, achieving a remarkable 3% average accuracy increase on the ImageNet dataset compared to the prior state-of-the-art. Additionally, we find that fine-tuning the self-supervised pre-trained backbone significantly boosts performance over the default in prior literature. After our paper is accepted, we will release the code.

Neural networks have become a prominent approach to solve inverse problems in recent years. While a plethora of such methods was developed to solve inverse problems empirically, we are still lacking clear theoretical guarantees for these methods. On the other hand, many works proved convergence to optimal solutions of neural networks in a more general setting using overparametrization as a way to control the Neural Tangent Kernel. In this work we investigate how to bridge these two worlds and we provide deterministic convergence and recovery guarantees for the class of unsupervised feedforward multilayer neural networks trained to solve inverse problems. We also derive overparametrization bounds under which a two-layers Deep Inverse Prior network with smooth activation function will benefit from our guarantees.

The problem of non-monotone $k$-submodular maximization under a knapsack constraint ($\kSMK$) over the ground set size $n$ has been raised in many applications in machine learning, such as data summarization, information propagation, etc. However, existing algorithms for the problem are facing questioning of how to overcome the non-monotone case and how to fast return a good solution in case of the big size of data. This paper introduces two deterministic approximation algorithms for the problem that competitively improve the query complexity of existing algorithms. Our first algorithm, $\LAA$, returns an approximation ratio of $1/19$ within $O(nk)$ query complexity. The second one, $\RLA$, improves the approximation ratio to $1/5-\epsilon$ in $O(nk)$ queries, where $\epsilon$ is an input parameter. Our algorithms are the first ones that provide constant approximation ratios within only $O(nk)$ query complexity for the non-monotone objective. They, therefore, need fewer the number of queries than state-of-the-the-art ones by a factor of $\Omega(\log n)$. Besides the theoretical analysis, we have evaluated our proposed ones with several experiments in some instances: Influence Maximization and Sensor Placement for the problem. The results confirm that our algorithms ensure theoretical quality as the cutting-edge techniques and significantly reduce the number of queries.

Disease progression simulation is a crucial area of research that has significant implications for clinical diagnosis, prognosis, and treatment. One major challenge in this field is the lack of continuous medical imaging monitoring of individual patients over time. To address this issue, we develop a novel framework termed Progressive Image Editing (PIE) that enables controlled manipulation of disease-related image features, facilitating precise and realistic disease progression simulation. Specifically, we leverage recent advancements in text-to-image generative models to simulate disease progression accurately and personalize it for each patient. We theoretically analyze the iterative refining process in our framework as a gradient descent with an exponentially decayed learning rate. To validate our framework, we conduct experiments in three medical imaging domains. Our results demonstrate the superiority of PIE over existing methods such as Stable Diffusion Walk and Style-Based Manifold Extrapolation based on CLIP score (Realism) and Disease Classification Confidence (Alignment). Our user study collected feedback from 35 veteran physicians to assess the generated progressions. Remarkably, 76.2% of the feedback agrees with the fidelity of the generated progressions. To our best knowledge, PIE is the first of its kind to generate disease progression images meeting real-world standards. It is a promising tool for medical research and clinical practice, potentially allowing healthcare providers to model disease trajectories over time, predict future treatment responses, and improve patient outcomes.

Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefit from improved model accuracy due to pooled resources. Recently proposed Neural Graphical Models (NGMs) are Probabilistic Graphical models that utilize the expressive power of neural networks to learn complex non-linear dependencies between the input features. They learn to capture the underlying data distribution and have efficient algorithms for inference and sampling. We develop a FL framework which maintains a global NGM model that learns the averaged information from the local NGM models while keeping the training data within the client's environment. Our design, FedNGMs, avoids the pitfalls and shortcomings of neuron matching frameworks like Federated Matched Averaging that suffers from model parameter explosion. Our global model size remains constant throughout the process. In the cases where clients have local variables that are not part of the combined global distribution, we propose a `Stitching' algorithm, which personalizes the global NGM models by merging the additional variables using the client's data. FedNGM is robust to data heterogeneity, large number of participants, and limited communication bandwidth.

With the rapid development of big data and computing devices, low-latency automatic trading platforms based on real-time information acquisition have become the main components of the stock trading market, so the topic of quantitative trading has received widespread attention. And for non-strongly efficient trading markets, human emotions and expectations always dominate market trends and trading decisions. Therefore, this paper starts from the theory of emotion, taking East Money as an example, crawling user comment titles data from its corresponding stock bar and performing data cleaning. Subsequently, a natural language processing model BERT was constructed, and the BERT model was fine-tuned using existing annotated data sets. The experimental results show that the fine-tuned model has different degrees of performance improvement compared to the original model and the baseline model. Subsequently, based on the above model, the user comment data crawled is labeled with emotional polarity, and the obtained label information is combined with the Alpha191 model to participate in regression, and significant regression results are obtained. Subsequently, the regression model is used to predict the average price change for the next five days, and use it as a signal to guide automatic trading. The experimental results show that the incorporation of emotional factors increased the return rate by 73.8\% compared to the baseline during the trading period, and by 32.41\% compared to the original alpha191 model. Finally, we discuss the advantages and disadvantages of incorporating emotional factors into quantitative trading, and give possible directions for further research in the future.

This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Incorporating a high-level policy for global task distribution and a low-level policy for real-time adaptations as a safety mechanism, our approach integrates the Similarity Attention-based Encoder (SABE) for task prioritization and an MLP estimator for energy consumption forecasting. Integrating this mechanism creates a safe and fault-tolerant system for CubeSat task scheduling. Simulation results validate the Hierarchical Reinforcement Learning superior convergence and task success rate, outperforming both the MADDPG model and traditional random scheduling across multiple CubeSat configurations.  ( 2 min )

Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic `0' and `1', AQFP devices serve as excellent carriers for binary neural network (BNN) computations. Although recent research has made initial strides toward developing an AQFP-based BNN accelerator, several critical challenges remain, preventing the design from being a comprehensive solution. In this paper, we propose SupeRBNN, an AQFP-based randomized BNN acceleration framework that leverages software-hardware co-optimization to eventually make the AQFP devices a feasible solution for BNN acceleration. Specifically, we investigate the randomized behavior of the AQFP devices and analyze the impact of crossbar size on current attenuation, subsequently formulating the current amplitude into the values suitable for use in BNN computation. To tackle the accumulation problem and improve overall hardware performance, we propose a stochastic computing-based accumulation module and a clocking scheme adjustment-based circuit optimization method. We validate our SupeRBNN framework across various datasets and network architectures, comparing it with implementations based on different technologies, including CMOS, ReRAM, and superconducting RSFQ/ERSFQ. Experimental results demonstrate that our design achieves an energy efficiency of approximately 7.8x10^4 times higher than that of the ReRAM-based BNN framework while maintaining a similar level of model accuracy. Furthermore, when compared with superconductor-based counterparts, our framework demonstrates at least two orders of magnitude higher energy efficiency.  ( 3 min )

We introduce the Bittensor Language Model, called "BTLM-3B-8K", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from the SlimPajama dataset with a mixture of 2,048 and 8,192 context lengths. BTLM-3B-8K outperforms all existing 3B parameter models by 2-5.5% across downstream tasks. BTLM-3B-8K is even competitive with some 7B parameter models. Additionally, BTLM-3B-8K provides excellent long context performance, outperforming MPT-7B-8K and XGen-7B-8K on tasks up to 8,192 context length. We trained the model on a cleaned and deduplicated SlimPajama dataset; aggressively tuned the \textmu P hyperparameters and schedule; used ALiBi position embeddings; and adopted the SwiGLU nonlinearity. On Hugging Face, the most popular models have 7B parameters, indicating that users prefer the quality-size ratio of 7B models. Compacting the 7B parameter model to one with 3B parameters, with little performance impact, is an important milestone. BTLM-3B-8K needs only 3GB of memory with 4-bit precision and takes 2.5x less inference compute than 7B models, helping to open up access to a powerful language model on mobile and edge devices. BTLM-3B-8K is available under an Apache 2.0 license on Hugging Face: https://huggingface.co/cerebras/btlm-3b-8k-base.

In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines. However, both contribute significantly to user satisfaction, underpinning our assumption that it relies on both an item's relevance and its presentation, particularly in the case of visual creatives. In response, we introduce the task of {\itshape Generative Creative Optimization (GCO)}, which proposes the use of generative models for creative generation that incorporate user interests, and {\itshape AdBooster}, a model for personalized ad creatives based on the Stable Diffusion outpainting architecture. This model uniquely incorporates user interests both during fine-tuning and at generation time. To further improve AdBooster's performance, we also introduce an automated data augmentation pipeline. Through our experiments on simulated data, we validate AdBooster's effectiveness in generating more relevant creatives than default product images, showing its potential of enhancing user engagement.

A common formulation of constrained reinforcement learning involves multiple rewards that must individually accumulate to given thresholds. In this class of problems, we show a simple example in which the desired optimal policy cannot be induced by any weighted linear combination of rewards. Hence, there exist constrained reinforcement learning problems for which neither regularized nor classical primal-dual methods yield optimal policies. This work addresses this shortcoming by augmenting the state with Lagrange multipliers and reinterpreting primal-dual methods as the portion of the dynamics that drives the multipliers evolution. This approach provides a systematic state augmentation procedure that is guaranteed to solve reinforcement learning problems with constraints. Thus, as we illustrate by an example, while previous methods can fail at finding optimal policies, running the dual dynamics while executing the augmented policy yields an algorithm that provably samples actions from the optimal policy.  ( 2 min )

Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees). Furthermore, our algorithms adapt to model misspecification and extend to the continuous arm settings. These advantages come from constructing and relying on "conformal arm sets" (CASs), which provide a set of arms at every context that encompass the context-specific optimal arm with some probability across the context distribution. Our positive results on simple and cumulative regret guarantees are contrasted by a negative result, which shows that an algorithm can't achieve instance-dependent simple regret guarantees while simultaneously achieving minimax optimal cumulative regret guarantees.

In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary. Existing methods typically train on supervised datasets with limited samples (approximately a few hundred), which can lead to overfitting. To address this, the low-rank adaptation technique is adopted for foundation model fine-tuning and proposed corresponding prompt point generation strategies to process various segmentation tasks on OCTA datasets. This method is named SAM-OCTA and has been experimented on the publicly available OCTA-500 dataset. While achieving state-of-the-art performance metrics, this method accomplishes local vessel segmentation as well as effective artery-vein segmentation, which was not well-solved in previous works. The code is available at: https://github.com/ShellRedia/SAM-OCTA.

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations such as priority, minimum and maximum activation events, execution time-frames, periods, and execution windows, as well as constraints on the satellite's power resources and the complexity of energy harvesting and management. The ONTS problem has been approached using conventional mathematical formulations and exact methods, but their applicability to challenging cases of the problem is limited. This study examines the use of GNNs in this context, which has been effectively applied to optimization problems such as the traveling salesman, scheduling, and facility placement problems. More specifically, we investigate whether GNNs can learn the complex structure of the ONTS problem with respect to feasibility and optimality of candidate solutions. Furthermore, we evaluate using GNN-based heuristic solutions to provide better solutions (w.r.t. the objective value) to the ONTS problem and reduce the optimization cost. Our experiments show that GNNs are not only able to learn feasibility and optimality for instances of the ONTS problem, but they can generalize to harder instances than those seen during training. Furthermore, the GNN-based heuristics improved the expected objective value of the best solution found under the time limit in 45%, and reduced the expected time to find a feasible solution in 35%, when compared to the SCIP (Solving Constraint Integer Programs) solver in its off-the-shelf configuration

Privacy-preserving crowd density analysis finds application across a wide range of scenarios, substantially enhancing smart building operation and management while upholding privacy expectations in various spaces. We propose a non-speech audio-based approach for crowd analytics, leveraging a transformer-based model. Our results demonstrate that non-speech audio alone can be used to conduct such analysis with remarkable accuracy. To the best of our knowledge, this is the first time when non-speech audio signals are proposed for predicting occupancy. As far as we know, there has been no other similar approach of its kind prior to this. To accomplish this, we deployed our sensor-based platform in the waiting room of a large hospital with IRB approval over a period of several months to capture non-speech audio and thermal images for the training and evaluation of our models. The proposed non-speech-based approach outperformed the thermal camera-based model and all other baselines. In addition to demonstrating superior performance without utilizing speech audio, we conduct further analysis using differential privacy techniques to provide additional privacy guarantees. Overall, our work demonstrates the viability of employing non-speech audio data for accurate occupancy estimation, while also ensuring the exclusion of speech-related content and providing robust privacy protections through differential privacy guarantees.

The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning. While promising theoretical results have been established, the empirical performance of algorithms leveraging a decision-aware loss has been lacking, especially in continuous control problems. In this paper, we present a study on the necessary components for decision-aware reinforcement learning models and we showcase design choices that enable well-performing algorithms. To this end, we provide a theoretical and empirical investigation into prominent algorithmic ideas in the field. We highlight that empirical design decisions established in the MuZero line of works are vital to achieving good performance for related algorithms, and we showcase differences in behavior between different instantiations of value-aware algorithms in stochastic environments. Using these insights, we propose the Latent Model-Based Decision-Aware Actor-Critic framework ($\lambda$-AC) for decision-aware model-based reinforcement learning in continuous state-spaces and highlight important design choices in different environments.

We study batched bandit experiments and consider the problem of inference conditional on the realized stopping time, assignment probabilities, and target parameter, where all of these may be chosen adaptively using information up to the last batch of the experiment. Absent further restrictions on the experiment, we show that inference using only the results of the last batch is optimal. When the adaptive aspects of the experiment are known to be location-invariant, in the sense that they are unchanged when we shift all batch-arm means by a constant, we show that there is additional information in the data, captured by one additional linear function of the batch-arm means. In the more restrictive case where the stopping time, assignment probabilities, and target parameter are known to depend on the data only through a collection of polyhedral events, we derive computationally tractable and optimal conditional inference procedures.  ( 2 min )

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric verification, they address different detection tasks and are hence typically evaluated separately. Evidence shows that this approach is suboptimal. We introduce a new metric for the joint evaluation of PAD solutions operating in situ with biometric verification. In contrast to the tandem detection cost function proposed recently, the new tandem equal error rate (t-EER) is parameter free. The combination of two classifiers nonetheless leads to a \emph{set} of operating points at which false alarm and miss rates are equal and also dependent upon the prevalence of attacks. We therefore introduce the \emph{concurrent} t-EER, a unique operating point which is invariable to the prevalence of attacks. Using both modality (and even application) agnostic simulated scores, as well as real scores for a voice biometrics application, we demonstrate application of the t-EER to a wide range of biometric system evaluations under attack. The proposed approach is a strong candidate metric for the tandem evaluation of PAD systems and biometric comparators.

We present ForceSight, a system for text-guided mobile manipulation that predicts visual-force goals using a deep neural network. Given a single RGBD image combined with a text prompt, ForceSight determines a target end-effector pose in the camera frame (kinematic goal) and the associated forces (force goal). Together, these two components form a visual-force goal. Prior work has demonstrated that deep models outputting human-interpretable kinematic goals can enable dexterous manipulation by real robots. Forces are critical to manipulation, yet have typically been relegated to lower-level execution in these systems. When deployed on a mobile manipulator equipped with an eye-in-hand RGBD camera, ForceSight performed tasks such as precision grasps, drawer opening, and object handovers with an 81% success rate in unseen environments with object instances that differed significantly from the training data. In a separate experiment, relying exclusively on visual servoing and ignoring force goals dropped the success rate from 90% to 45%, demonstrating that force goals can significantly enhance performance. The appendix, videos, code, and trained models are available at https://force-sight.github.io/.

Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions were subset into two biomechanically independent components - a set of wrist directions and a set of finger modifiers. To maintain fast calibration time, we train models for each component using only individual gestures, and extrapolate to the full product space of combination gestures by generating synthetic data. We collected a supervised dataset with high-confidence ground truth labels in which subjects performed combination gestures while holding a joystick, and conducted experiments to analyze the impact of model architectures, classifier algorithms, and synthetic data generation strategies on the performance of the proposed approach. Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time. Significance: sEMG-based gesture recognition has applications in human-computer interaction, virtual reality, and the control of robotic and prosthetic devices. Existing approaches require exhaustive model calibration. The proposed approach increases expressivity without requiring users to demonstrate all combination gesture classes. Our results may be extended to larger gesture vocabularies and more complicated model architectures.  ( 2 min )

Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify the degree of miscalibration, and reliability diagrams visualize the structure of this miscalibration. However, the most common constructions of reliability diagrams and calibration measures -- binning and ECE -- both suffer from well-known flaws (e.g. discontinuity). We show that a simple modification fixes both constructions: first smooth the observations using an RBF kernel, then compute the Expected Calibration Error (ECE) of this smoothed function. We prove that with a careful choice of bandwidth, this method yields a calibration measure that is well-behaved in the sense of (B{\l}asiok, Gopalan, Hu, and Nakkiran 2023a) -- a consistent calibration measure. We call this measure the SmoothECE. Moreover, the reliability diagram obtained from this smoothed function visually encodes the SmoothECE, just as binned reliability diagrams encode the BinnedECE. We also provide a Python package with simple, hyperparameter-free methods for measuring and plotting calibration: `pip install relplot\`.

Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature. These strategies are crucial for practitioners in various domains, such as marketing, telecommunications, health care and finance. This paper presents a comprehensive treatment of the subject, starting from firm theoretical foundations and highlighting the parameters that influence the performance of the uplift and predictive approaches. The focus of the paper is on a binary outcome case and a binary action, and the paper presents a theoretical analysis of uplift modeling, comparing it with the classical predictive approach. The main research contributions of the paper include a new formulation of the measure of profit, a formal proof of the convergence of the uplift curve to the measure of profit ,and an illustration, through simulations, of the conditions under which predictive approaches still outperform uplift modeling. We show that the mutual information between the features and the outcome plays a significant role, along with the variance of the estimators, the distribution of the potential outcomes and the underlying costs and benefits of the treatment and the outcome.  ( 2 min )

Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance. However, FL faces two crucial challenges: the diverse nature of data held by individual clients and the vulnerability of the FL system to security breaches. This paper introduces an innovative solution named Estimated Mean Aggregation (EMA) that not only addresses these challenges but also provides a fundamental reference point as a $\mathsf{baseline}$ for advanced aggregation techniques in FL systems. EMA's significance lies in its dual role: enhancing model security by effectively handling malicious outliers through trimmed means and uncovering data heterogeneity to ensure that trained models are adaptable across various client datasets. Through a wealth of experiments, EMA consistently demonstrates high accuracy and area under the curve (AUC) compared to alternative methods, establishing itself as a robust baseline for evaluating the effectiveness and security of FL aggregation methods. EMA's contributions thus offer a crucial step forward in advancing the efficiency, security, and versatility of decentralized deep learning in the context of FL.  ( 2 min )

We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048. In this paper, we speed up the context extension of LLMs in two aspects. On the one hand, although dense global attention is needed during inference, fine-tuning the model can be effectively and efficiently done by sparse local attention. The proposed shift short attention effectively enables context extension, leading to non-trivial computation saving with similar performance to fine-tuning with vanilla attention. Particularly, it can be implemented with only two lines of code in training, while being optional in inference. On the other hand, we revisit the parameter-efficient fine-tuning regime for context expansion. Notably, we find that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA demonstrates strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like FlashAttention-2. In addition, to make LongLoRA practical, we collect a dataset, LongQA, for supervised fine-tuning. It contains more than 3k long context question-answer pairs.  ( 3 min )

Brain tumors are collections of abnormal cells that can develop into masses or clusters. Because they have the potential to infiltrate other tissues, they pose a risk to the patient. The main imaging technique used, MRI, may be able to identify a brain tumor with accuracy. The fast development of Deep Learning methods for use in computer vision applications has been facilitated by a vast amount of training data and improvements in model construction that offer better approximations in a supervised setting. The need for these approaches has been the main driver of this expansion. Deep learning methods have shown promise in improving the precision of brain tumor detection and classification using magnetic resonance imaging (MRI). The study on the use of deep learning techniques, especially ResNet50, for brain tumor identification is presented in this abstract. As a result, this study investigates the possibility of automating the detection procedure using deep learning techniques. In this study, I utilized five transfer learning models which are VGG16, VGG19, DenseNet121, ResNet50 and YOLO V4 where ResNet50 provide the best or highest accuracy 99.54%. The goal of the study is to guide researchers and medical professionals toward powerful brain tumor detecting systems by employing deep learning approaches by way of this evaluation and analysis.  ( 2 min )

In this work, we introduce Boolformer, the first Transformer architecture trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for complex functions which were not seen during training, when provided a clean truth table. Then, we demonstrate its ability to find approximate expressions when provided incomplete and noisy observations. We evaluate the Boolformer on a broad set of real-world binary classification datasets, demonstrating its potential as an interpretable alternative to classic machine learning methods. Finally, we apply it to the widespread task of modelling the dynamics of gene regulatory networks. Using a recent benchmark, we show that Boolformer is competitive with state-of-the art genetic algorithms with a speedup of several orders of magnitude. Our code and models are available publicly.  ( 2 min )

Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.  ( 2 min )

The training of neural encoders via deep learning necessitates a differentiable channel model due to the backpropagation algorithm. This requirement can be sidestepped by approximating either the channel distribution or its gradient through pilot signals in real-world scenarios. The initial approach draws upon the latest advancements in image generation, utilizing generative adversarial networks (GANs) or their enhanced variants to generate channel distributions. In this paper, we address this channel approximation challenge with diffusion models, which have demonstrated high sample quality in image generation. We offer an end-to-end channel coding framework underpinned by diffusion models and propose an efficient training algorithm. Our simulations with various channel models establish that our diffusion models learn the channel distribution accurately, thereby achieving near-optimal end-to-end symbol error rates (SERs). We also note a significant advantage of diffusion models: A robust generalization capability in high signal-to-noise ratio regions, in contrast to GAN variants that suffer from error floor. Furthermore, we examine the trade-off between sample quality and sampling speed, when an accelerated sampling algorithm is deployed, and investigate the effect of the noise scheduling on this trade-off. With an apt choice of noise scheduling, sampling time can be significantly reduced with a minor increase in SER.  ( 2 min )

For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance. Semantics here is a measure of the usefulness of information transmissions. Semantic-aware transmission scheduling of a large system often involves a large decision-making space, and the optimal policy cannot be obtained by existing algorithms effectively. In this paper, we first investigate the fundamental properties of the optimal semantic-aware scheduling policy and then develop advanced deep reinforcement learning (DRL) algorithms by leveraging the theoretical guidelines. Our numerical results show that the proposed algorithms can substantially reduce training time and enhance training performance compared to benchmark algorithms.

Autistic Spectrum Disorder (ASD) is a neurological disease characterized by difficulties with social interaction, communication, and repetitive activities. The severity of these difficulties varies, and those with this diagnosis face unique challenges. While its primary origin lies in genetics, identifying and addressing it early can contribute to the enhancement of the condition. In recent years, machine learning-driven intelligent diagnosis has emerged as a supplement to conventional clinical approaches, aiming to address the potential drawbacks of time-consuming and costly traditional methods. In this work, we utilize different machine learning algorithms to find the most significant traits responsible for ASD and to automate the diagnostic process. We study six classification models to see which model works best to identify ASD and also study five popular clustering methods to get a meaningful insight of these ASD datasets. To find the best classifier for these binary datasets, we evaluate the models using accuracy, precision, recall, specificity, F1-score, AUC, kappa and log loss metrics. Our evaluation demonstrates that five out of the six selected models perform exceptionally, achieving a 100% accuracy rate on the ASD datasets when hyperparameters are meticulously tuned for each model. As almost all classification models are able to get 100% accuracy, we become interested in observing the underlying insights of the datasets by implementing some popular clustering algorithms on these datasets. We calculate Normalized Mutual Information (NMI), Adjusted Rand Index (ARI) & Silhouette Coefficient (SC) metrics to select the best clustering models. Our evaluation finds that spectral clustering outperforms all other benchmarking clustering models in terms of NMI & ARI metrics and it also demonstrates comparability to the optimal SC achieved by k-means.

Recently, studies on machine learning have focused on methods that use symmetry implicit in a specific manifold as an inductive bias. Grassmann manifolds provide the ability to handle fundamental shapes represented as shape spaces, enabling stable shape analysis. In this paper, we present a novel approach in which we establish the theoretical foundations for learning distributions on the Grassmann manifold via continuous normalization flows, with the explicit goal of generating stable shapes. Our approach facilitates more robust generation by effectively eliminating the influence of extraneous transformations, such as rotations and inversions, through learning and generating within a Grassmann manifolds designed to accommodate the essential shape information of the object. The experimental results indicated that the proposed method can generate high-quality samples by capturing the data structure. Furthermore, the proposed method significantly outperformed state-of-the-art methods in terms of the log-likelihood or evidence lower bound. The results obtained are expected to stimulate further research in this field, leading to advances for stable shape generation and analysis.

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The problem is typically solved by modeling complex spatio-temporal correlations in traffic data using spatio-temporal graph neural networks (GNNs). However, the performance of these methods is still far from satisfactory since GNNs usually have limited representation capacity when it comes to complex traffic networks. Graphs, by nature, fall short in capturing non-pairwise relations. Even worse, existing methods follow the paradigm of message passing that aggregates neighborhood information linearly, which fails to capture complicated spatio-temporal high-order interactions. To tackle these issues, in this paper, we propose a novel model named Dynamic Hypergraph Structure Learning (DyHSL) for traffic flow prediction. To learn non-pairwise relationships, our DyHSL extracts hypergraph structural information to model dynamics in the traffic networks, and updates each node representation by aggregating messages from its associated hyperedges. Additionally, to capture high-order spatio-temporal relations in the road network, we introduce an interactive graph convolution block, which further models the neighborhood interaction for each node. Finally, we integrate these two views into a holistic multi-scale correlation extraction module, which conducts temporal pooling with different scales to model different temporal patterns. Extensive experiments on four popular traffic benchmark datasets demonstrate the effectiveness of our proposed DyHSL compared with a broad range of competing baselines.  ( 3 min )

Linking sheet music images to audio recordings remains a key problem for the development of efficient cross-modal music retrieval systems. One of the fundamental approaches toward this task is to learn a cross-modal embedding space via deep neural networks that is able to connect short snippets of audio and sheet music. However, the scarcity of annotated data from real musical content affects the capability of such methods to generalize to real retrieval scenarios. In this work, we investigate whether we can mitigate this limitation with self-supervised contrastive learning, by exposing a network to a large amount of real music data as a pre-training step, by contrasting randomly augmented views of snippets of both modalities, namely audio and sheet images. Through a number of experiments on synthetic and real piano data, we show that pre-trained models are able to retrieve snippets with better precision in all scenarios and pre-training configurations. Encouraged by these results, we employ the snippet embeddings in the higher-level task of cross-modal piece identification and conduct more experiments on several retrieval configurations. In this task, we observe that the retrieval quality improves from 30% up to 100% when real music data is present. We then conclude by arguing for the potential of self-supervised contrastive learning for alleviating the annotated data scarcity in multi-modal music retrieval models.

The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting traffic participants. To address this, we propose a method that utilizes unlabeled repeated traversals of multiple locations to adapt object detectors to new driving environments. By incorporating statistics computed from repeated LiDAR scans, we guide the adaptation process effectively. Our approach enhances LiDAR-based detection models using spatial quantized historical features and introduces a lightweight regression head to leverage the statistics for feature regularization. Additionally, we leverage the statistics for a novel self-training process to stabilize the training. The framework is detector model-agnostic and experiments on real-world datasets demonstrate significant improvements, achieving up to a 20-point performance gain, especially in detecting pedestrians and distant objects. Code is available at https://github.com/zhangtravis/Hist-DA.

Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via random Fourier features (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.

Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-modal data is the key to a better audiovisual modal. We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video. More concretely, such data is inherently multi-modal according to both audio and visual cues, which proceed in a strict chronological order. It indicates that temporal information is important in multi-modal acoustic event modeling for both intra- and inter-modal. However, existing methods deal with each modal feature independently and simply fuse them together, which neglects the mining of temporal relation and thus leads to sub-optimal performance. With this motivation, we propose a Temporal Multi-modal graph learning method for Acoustic event Classification, called TMac, by modeling such temporal information via graph learning techniques. In particular, we construct a temporal graph for each acoustic event, dividing its audio data and video data into multiple segments. Each segment can be considered as a node, and the temporal relationships between nodes can be considered as timestamps on their edges. In this case, we can smoothly capture the dynamic information in intra-modal and inter-modal. Several experiments are conducted to demonstrate TMac outperforms other SOTA models in performance. Our code is available at https://github.com/MGitHubL/TMac.

Anticancer peptides (ACPs) are a group of peptides that exhibite antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec and FastText are evaluated as word embedding techniques for the purpose of extracting peptide sequences. Then, the output of word embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and Independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for Independent dataset, thence determining new state-of-the-art.

We formulate a data independent latent space regularisation constraint for general unsupervised autoencoders. The regularisation rests on sampling the autoencoder Jacobian in Legendre nodes, being the centre of the Gauss-Legendre quadrature. Revisiting this classic enables to prove that regularised autoencoders ensure a one-to-one re-embedding of the initial data manifold to its latent representation. Demonstrations show that prior proposed regularisation strategies, such as contractive autoencoding, cause topological defects already for simple examples, and so do convolutional based (variational) autoencoders. In contrast, topological preservation is ensured already by standard multilayer perceptron neural networks when being regularised due to our contribution. This observation extends through the classic FashionMNIST dataset up to real world encoding problems for MRI brain scans, suggesting that, across disciplines, reliable low dimensional representations of complex high-dimensional datasets can be delivered due to this regularisation technique.  ( 2 min )

Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home. Traditional model-based localization suffers from significant computational overhead, so fingerprint localization is getting increasing attention, which needs lower computation cost after the fingerprint database is built. However, the accuracy of indoor localization is limited by the complicated indoor environment which brings the multipath signal refraction. In this paper, we provided a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain by predicting the channel state information (CSI) values from another transmitting channel and spliced the multi-band information together to get more precise localization results. We tested our proposed scheme on COST 2100 simulation data and real time orthogonal frequency division multiplexing (OFDM) WiFi data collected from an office scenario.  ( 2 min )

Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains, i.e., without accounting for varying driving behaviors across locations or model scalability. In this work, we propose AnyD, a single geographically-aware conditional imitation learning (CIL) model that can efficiently learn from heterogeneous and globally distributed data with dynamic environmental, traffic, and social characteristics. Our key insight is to introduce a high-capacity geo-location-based channel attention mechanism that effectively adapts to local nuances while also flexibly modeling similarities among regions in a data-driven manner. By optimizing a contrastive imitation objective, our proposed approach can efficiently scale across inherently imbalanced data distributions and location-dependent events. We demonstrate the benefits of our AnyD agent across multiple datasets, cities, and scalable deployment paradigms, i.e., centralized, semi-supervised, and distributed agent training. Specifically, AnyD outperforms CIL baselines by over 14% in open-loop evaluation and 30% in closed-loop testing on CARLA.  ( 2 min )

Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications. The state-of-the-art -- in Bayesian sparsification of deep neural networks -- combines structural shrinkage priors on model weights with an approximate inference scheme based on black-box stochastic variational inference. However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates. In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights. As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model. Our comparative study highlights the computational efficiency and the pruning rate of the BMR method relative to the established stochastic variational inference (SVI) scheme, when applied to the full hierarchical generative model. We illustrate the potential of BMR to prune model parameters across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers.

Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function $h$, and possibly non-Lipschitz. We analyze the regret of online mirror descent with $h$. Then, based on the result, we prove the following in a unified manner. Denote by $T$ the time horizon and $d$ the parameter dimension. 1. For online portfolio selection, the regret of $\widetilde{\text{EG}}$, a variant of exponentiated gradient due to Helmbold et al., is $\tilde{O} ( T^{2/3} d^{1/3} )$ when $T > 4 d / \log d$. This improves on the original $\tilde{O} ( T^{3/4} d^{1/2} )$ regret bound for $\widetilde{\text{EG}}$. 2. For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$. The regret bound is the same as that of Soft-Bayes due to Orseau et al. up to logarithmic terms. 3. For online learning quantum states with the logarithmic loss, the regret of online mirror descent with the log-determinant function is also $\tilde{O} ( \sqrt{T d} )$. Its per-iteration time is shorter than all existing algorithms we know.  ( 3 min )

Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the problem by representing local tasks using different statistics and relating them through a simple aggregation mechanism. However, these approaches suffer from limited efficiency from two aspects: low quality of task-relatedness quantification and inefficacy of exploiting the collaboration structure. To address these issues, we propose FedGKD, a novel federated GNN framework that utilizes a novel client-side graph dataset distillation method to extract task features that better describe task-relatedness, and introduces a novel server-side aggregation mechanism that is aware of the global collaboration structure. We conduct extensive experiments on six real-world datasets of different scales, demonstrating our framework's outperformance.

Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving the efficacy of SAEAs through the development of proficient model-assisted selection methods. However, generating high-quality solutions is a prerequisite for selection. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thus impacting the quality of offspring solutions. This is a frequently encountered issue, yet it has not gained widespread attention. This paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models for the selection of the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.

Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM) have shown that transformer LM encoder based similarity metrics transfer well for QA evaluation, but they are limited by the usage of a single correct reference answer. We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation), using multiple reference answers (combining multiple correct and incorrect references) for sentence-form QA. We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems, across multiple academic and industrial datasets, and show that it outperforms previous baselines and obtains the highest correlation with human annotations.

Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process large-scale point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feed forward methods are considerably faster, running on the order of tens to hundreds of milliseconds for large-scale point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feed forward model. Our instantiation of this framework, ZeroFlow, achieves state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow Challenge while using zero human labels by simply training on large-scale, diverse unlabeled data. At test-time, ZeroFlow is over 1000$\times$ faster than label-free state-of-the-art optimization-based methods on large-scale point clouds and over 1000$\times$ cheaper to train on unlabeled data compared to the cost of human annotation of that data. To facilitate further research, we will release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.

Recently, large multimodal models, such as CLIP and Stable Diffusion have experimented tremendous successes in both foundations and applications. However, as these models increase in parameter size and computational requirements, it becomes more challenging for users to personalize them for specific tasks or preferences. In this work, we address the problem of adapting the previous models towards sets of particular human preferences, aligning the retrieved or generated images with the preferences of the user. We leverage the Bradley-Terry preference model to develop a fast adaptation method that efficiently fine-tunes the original model, with few examples and with minimal computing resources. Extensive evidence of the capabilities of this framework is provided through experiments in different domains related to multimodal text and image understanding, including preference prediction as a reward model, and generation tasks.

Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on tactile sensing fails to provide adequate cues for reasoning about objects' spatial configurations, limiting the ability to correct errors and adapt to changing situations. In this paper, we present Tactile Adaptation from Visual Incentives (TAVI), a new framework that enhances tactile-based dexterity by optimizing dexterous policies using vision-based rewards. First, we use a contrastive-based objective to learn visual representations. Next, we construct a reward function using these visual representations through optimal-transport based matching on one human demonstration. Finally, we use online reinforcement learning on our robot to optimize tactile-based policies that maximize the visual reward. On six challenging tasks, such as peg pick-and-place, unstacking bowls, and flipping slender objects, TAVI achieves a success rate of 73% using our four-fingered Allegro robot hand. The increase in performance is 108% higher than policies using tactile and vision-based rewards and 135% higher than policies without tactile observational input. Robot videos are best viewed on our project website: https://see-to-touch.github.io/.

Generating multi-instrument music from symbolic music representations is an important task in Music Information Retrieval (MIR). A central but still largely unsolved problem in this context is musically and acoustically informed control in the generation process. As the main contribution of this work, we propose enhancing control of multi-instrument synthesis by conditioning a generative model on a specific performance and recording environment, thus allowing for better guidance of timbre and style. Building on state-of-the-art diffusion-based music generative models, we introduce performance conditioning - a simple tool indicating the generative model to synthesize music with style and timbre of specific instruments taken from specific performances. Our prototype is evaluated using uncurated performances with diverse instrumentation and achieves state-of-the-art FAD realism scores while allowing novel timbre and style control. Our project page, including samples and demonstrations, is available at benadar293.github.io/midipm

Ensuring that analyses performed on a dataset are representative of the entire population is one of the central problems in statistics. Most classical techniques assume that the dataset is independent of the analyst's query and break down in the common setting where a dataset is reused for multiple, adaptively chosen, queries. This problem of \emph{adaptive data analysis} was formalized in the seminal works of Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014). We identify a remarkably simple set of assumptions under which the queries will continue to be representative even when chosen adaptively: The only requirements are that each query takes as input a random subsample and outputs few bits. This result shows that the noise inherent in subsampling is sufficient to guarantee that query responses generalize. The simplicity of this subsampling-based framework allows it to model a variety of real-world scenarios not covered by prior work. In addition to its simplicity, we demonstrate the utility of this framework by designing mechanisms for two foundational tasks, statistical queries and median finding. In particular, our mechanism for answering the broadly applicable class of statistical queries is both extremely simple and state of the art in many parameter regimes.

We demonstrate the first algorithms for the problem of regression for generalized linear models (GLMs) in the presence of additive oblivious noise. We assume we have sample access to examples $(x, y)$ where $y$ is a noisy measurement of $g(w^* \cdot x)$. In particular, \new{the noisy labels are of the form} $y = g(w^* \cdot x) + \xi + \epsilon$, where $\xi$ is the oblivious noise drawn independently of $x$ \new{and satisfies} $\Pr[\xi = 0] \geq o(1)$, and $\epsilon \sim \mathcal N(0, \sigma^2)$. Our goal is to accurately recover a \new{parameter vector $w$ such that the} function $g(w \cdot x)$ \new{has} arbitrarily small error when compared to the true values $g(w^* \cdot x)$, rather than the noisy measurements $y$. We present an algorithm that tackles \new{this} problem in its most general distribution-independent setting, where the solution may not \new{even} be identifiable. \new{Our} algorithm returns \new{an accurate estimate of} the solution if it is identifiable, and otherwise returns a small list of candidates, one of which is close to the true solution. Furthermore, we \new{provide} a necessary and sufficient condition for identifiability, which holds in broad settings. \new{Specifically,} the problem is identifiable when the quantile at which $\xi + \epsilon = 0$ is known, or when the family of hypotheses does not contain candidates that are nearly equal to a translated $g(w^* \cdot x) + A$ for some real number $A$, while also having large error when compared to $g(w^* \cdot x)$. This is the first \new{algorithmic} result for GLM regression \new{with oblivious noise} which can handle more than half the samples being arbitrarily corrupted. Prior work focused largely on the setting of linear regression, and gave algorithms under restrictive assumptions.

Privacy-preserving learning of gradient boosting decision trees (GBDT) has the potential for strong utility-privacy tradeoffs for tabular data, such as census data or medical meta data: classical GBDT learners can extract non-linear patterns from small sized datasets. The state-of-the-art notion for provable privacy-properties is differential privacy, which requires that the impact of single data points is limited and deniable. We introduce a novel differentially private GBDT learner and utilize four main techniques to improve the utility-privacy tradeoff. (1) We use an improved noise scaling approach with tighter accounting of privacy leakage of a decision tree leaf compared to prior work, resulting in noise that in expectation scales with $O(1/n)$, for $n$ data points. (2) We integrate individual R\'enyi filters to our method to learn from data points that have been underutilized during an iterative training process, which -- potentially of independent interest -- results in a natural yet effective insight to learning streams of non-i.i.d. data. (3) We incorporate the concept of random decision tree splits to concentrate privacy budget on learning leaves. (4) We deploy subsampling for privacy amplification. Our evaluation shows for the Abalone dataset ($<4k$ training data points) a $R^2$-score of $0.39$ for $\varepsilon=0.15$, which the closest prior work only achieved for $\varepsilon=10.0$. On the Adult dataset ($50k$ training data points) we achieve test error of $18.7\,\%$ for $\varepsilon=0.07$ which the closest prior work only achieved for $\varepsilon=1.0$. For the Abalone dataset for $\varepsilon=0.54$ we achieve $R^2$-score of $0.47$ which is very close to the $R^2$-score of $0.54$ for the nonprivate version of GBDT. For the Adult dataset for $\varepsilon=0.54$ we achieve test error $17.1\,\%$ which is very close to the test error $13.7\,\%$ of the nonprivate version of GBDT.

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, brute-force virtual screening using traditional tools such as docking becomes infeasible in terms of time and computational resources. Active learning and Bayesian optimization has recently been proven as effective methods of narrowing down the search space. An essential component in those methods is a surrogate machine learning model that is trained with a small subset of the library to predict the desired properties of compounds. Accurate model can achieve high sample efficiency by finding the most promising compounds with only a fraction of the whole library being virtually screened. In this study, we examined the performance of pretrained transformer-based language model and graph neural network in Bayesian optimization active learning framework. The best pretrained models identifies 58.97% of the top-50000 by docking score after screening only 0.6% of an ultra-large library containing 99.5 million compounds, improving 8% over previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Such model can serve as a boost to the accuracy and sample efficiency of active learning based molecule virtual screening.

We present a novel adversarial model for authentication systems that use gait patterns recorded by the inertial measurement unit (IMU) built into smartphones. The attack idea is inspired by and named after the concept of a dictionary attack on knowledge (PIN or password) based authentication systems. In particular, this work investigates whether it is possible to build a dictionary of IMUGait patterns and use it to launch an attack or find an imitator who can actively reproduce IMUGait patterns that match the target's IMUGait pattern. Nine physically and demographically diverse individuals walked at various levels of four predefined controllable and adaptable gait factors (speed, step length, step width, and thigh-lift), producing 178 unique IMUGait patterns. Each pattern attacked a wide variety of user authentication models. The deeper analysis of error rates (before and after the attack) challenges the belief that authentication systems based on IMUGait patterns are the most difficult to spoof; further research is needed on adversarial models and associated countermeasures.

Text-conditioned image generation models have recently achieved astonishing image quality and alignment results. Consequently, they are employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the web, they also produce unsafe content. As a contribution to the Adversarial Nibbler challenge, we distill a large set of over 1,000 potential adversarial inputs from existing safety benchmarks. Our analysis of the gathered prompts and corresponding images demonstrates the fragility of input filters and provides further insights into systematic safety issues in current generative image models.

Accurate load forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of dynamic power systems remains a challenge for traditional statistical models. For these reasons, time-series models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly deployed and often experience higher success. In this paper, we analyze the efficacy of the recently developed Transformer-based Neural Network model in Load forecasting. Transformer models have the potential to improve Load forecasting because of their ability to learn long-range dependencies derived from their Attention Mechanism. We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts. Differential Evolution provides scalable, robust, global solutions to non-differentiable, multi-objective, or constrained optimization problems. Our work compares the proposed Transformer based Neural Network model integrated with different metaheuristic algorithms by their performance in Load forecasting based on numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced Transformer-based Neural Network models in Load forecasting accuracy and provide optimal hyperparameters for each model.

Out-of-distribution (OOD) detection is crucial to modern deep learning applications by identifying and alerting about the OOD samples that should not be tested or used for making predictions. Current OOD detection methods have made significant progress when in-distribution (ID) and OOD samples are drawn from static distributions. However, this can be unrealistic when applied to real-world systems which often undergo continuous variations and shifts in ID and OOD distributions over time. Therefore, for an effective application in real-world systems, the development of OOD detection methods that can adapt to these dynamic and evolving distributions is essential. In this paper, we propose a novel and more realistic setting called continuously adaptive out-of-distribution (CAOOD) detection which targets on developing an OOD detection model that enables dynamic and quick adaptation to a new arriving distribution, with insufficient ID samples during deployment time. To address CAOOD, we develop meta OOD learning (MOL) by designing a learning-to-adapt diagram such that a good initialized OOD detection model is learned during the training process. In the testing process, MOL ensures OOD detection performance over shifting distributions by quickly adapting to new distributions with a few adaptations. Extensive experiments on several OOD benchmarks endorse the effectiveness of our method in preserving both ID classification accuracy and OOD detection performance on continuously shifting distributions.

We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form "A is B", it will not automatically generalize to the reverse direction "B is A". This is the Reversal Curse. For instance, if a model is trained on "Olaf Scholz was the ninth Chancellor of Germany", it will not automatically be able to answer the question, "Who was the ninth Chancellor of Germany?". Moreover, the likelihood of the correct answer ("Olaf Scholz") will not be higher than for a random name. Thus, models exhibit a basic failure of logical deduction and do not generalize a prevalent pattern in their training set (i.e. if "A is B'' occurs, "B is A" is more likely to occur). We provide evidence for the Reversal Curse by finetuning GPT-3 and Llama-1 on fictitious statements such as "Uriah Hawthorne is the composer of 'Abyssal Melodies'" and showing that they fail to correctly answer "Who composed 'Abyssal Melodies?'". The Reversal Curse is robust across model sizes and model families and is not alleviated by data augmentation. We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about real-world celebrities, such as "Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]" and the reverse "Who is Mary Lee Pfeiffer's son?". GPT-4 correctly answers questions like the former 79% of the time, compared to 33% for the latter. This shows a failure of logical deduction that we hypothesize is caused by the Reversal Curse. Code is available at https://github.com/lukasberglund/reversal_curse.

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because of information scarcity, noise, adversarial attacks, or discrepancies between the distribution in graph topology, features, and groundtruth labels. In this paper, we propose a bi-level optimization approach for learning the optimal graph structure via directly learning the Personalized PageRank propagation matrix as well as the downstream semi-supervised node classification simultaneously. We also explore a low-rank approximation model for further reducing the time complexity. Empirical evaluations show the superior efficacy and robustness of the proposed model over all baseline methods.

In recent years, datasets of paired audio and captions have enabled remarkable success in automatically generating descriptions for audio clips, namely Automated Audio Captioning (AAC). However, it is labor-intensive and time-consuming to collect a sufficient number of paired audio and captions. Motivated by the recent advances in Contrastive Language-Audio Pretraining (CLAP), we propose a weakly-supervised approach to train an AAC model assuming only text data and a pre-trained CLAP model, alleviating the need for paired target data. Our approach leverages the similarity between audio and text embeddings in CLAP. During training, we learn to reconstruct the text from the CLAP text embedding, and during inference, we decode using the audio embeddings. To mitigate the modality gap between the audio and text embeddings we employ strategies to bridge the gap during training and inference stages. We evaluate our proposed method on Clotho and AudioCaps datasets demonstrating its ability to achieve a relative performance of up to ~$83\%$ compared to fully supervised approaches trained with paired target data.

Structure learning is the crux of causal inference. Notably, causal discovery (CD) algorithms are brittle when data is scarce, possibly inferring imprecise causal relations that contradict expert knowledge -- especially when considering latent confounders. To aggravate the issue, most CD methods do not provide uncertainty estimates, making it hard for users to interpret results and improve the inference process. Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD. To solve these issues, we start by proposing to sample (causal) ancestral graphs proportionally to a belief distribution based on a score function, such as the Bayesian information criterion (BIC), using generative flow networks. Then, we leverage the diversity in candidate graphs and introduce an optimal experimental design to iteratively probe the expert about the relations among variables, effectively reducing the uncertainty of our belief over ancestral graphs. Finally, we update our samples to incorporate human feedback via importance sampling. Importantly, our method does not require causal sufficiency (i.e., unobserved confounders may exist). Experiments with synthetic observational data show that our method can accurately sample from distributions over ancestral graphs and that we can greatly improve inference quality with human aid.

We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task". Existing continual learning methods, such as Averaged Gradient Episodic Memory (A-GEM) and Orthogonal Gradient Descent (OGD), address catastrophic forgetting by minimizing the loss for the current task without increasing the loss for previous tasks. However, these methods assume the learner knows when the task changes, which is unrealistic in practice. In this paper, we alleviate the need to provide the algorithm with information about task changes by using an online clustering-based approach on a dynamically updated finite pool of samples or gradients. We thereby successfully counteract catastrophic forgetting in one of the hardest settings, namely: domain-incremental learning, a setting for which the problem was previously unsolved. We showcase the benefits of our approach by applying these ideas to projection-based methods, such as A-GEM and OGD, which lead to task-agnostic versions of them. Experiments on real datasets demonstrate the effectiveness of the proposed strategy and its promising performance compared to state-of-the-art methods.

New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future. The codes are available on GitHub: https://github.com/cruiseresearchgroup/forecasting-on-new-roads .

There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. In order to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net). First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module, and then the spatial-channel attention mechanism is used to strengthen the features, and the final convolution module can further learn the spatial-temporal information of the features. Finally, to account for inter-subject and cross-sessions differences, we employ a dynamic domain-adaptive strategy, the distance between features is reduced by introducing a Maximum Mean Discrepancy loss function, and the classification layer is fine-tuned by using part of the target domain data. We verify the performance of the proposed method on BCI competition IV 2a and OpenBMI datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.

A range of applications of multi-modal music information retrieval is centred around the problem of connecting large collections of sheet music (images) to corresponding audio recordings, that is, identifying pairs of audio and score excerpts that refer to the same musical content. One of the typical and most recent approaches to this task employs cross-modal deep learning architectures to learn joint embedding spaces that link the two distinct modalities - audio and sheet music images. While there has been steady improvement on this front over the past years, a number of open problems still prevent large-scale employment of this methodology. In this article we attempt to provide an insightful examination of the current developments on audio-sheet music retrieval via deep learning methods. We first identify a set of main challenges on the road towards robust and large-scale cross-modal music retrieval in real scenarios. We then highlight the steps we have taken so far to address some of these challenges, documenting step-by-step improvement along several dimensions. We conclude by analysing the remaining challenges and present ideas for solving these, in order to pave the way to a unified and robust methodology for cross-modal music retrieval.

Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is proposed to compress EEG signals. The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding parameters and scaling layers to give emphasis or de-emphasis on individual DCT coefficients. Finally, the one-by-one convolutional layer generates the latent space. The sparsity penalty-based cost function is employed to keep the feature map as sparse as possible in the latent space. The latent space data is transmitted to the receiver. The decoder module of the autoencoder is designed using the inverse DCT and two fully connected linear layers to improve the accuracy of data reconstruction. In comparison to other state-of-the-art methods, the proposed method significantly improves the average quality score in various data compression experiments.

The applications of personalized recommender systems are rapidly expanding: encompassing social media, online shopping, search engine results, and more. These systems offer a more efficient way to navigate the vast array of items available. However, alongside this growth, there has been increased recognition of the potential for algorithmic systems to exhibit and perpetuate biases, risking unfairness in personalized domains. In this work, we explicate the inherent tension between personalization and conventional implementations of fairness. As an alternative, we propose equity to achieve fairness in the context of epistemic utility. We provide a mapping between goals and practical implementations and detail policy recommendations across key stakeholders to forge a path towards achieving fairness in personalized systems.

We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family as hidden layer maps and a non-linear activation function applied to each hidden layer. Relying on Stone-Weierstrass theorems on weighted spaces, we can prove a global universal approximation result for generalizations of continuous functions going beyond the usual approximation on compact sets. This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks. As a further application of the weighted Stone-Weierstrass theorem we prove a global universal approximation result for linear functions of the signature. We also introduce the viewpoint of Gaussian process regression in this setting and show that the reproducing kernel Hilbert space of the signature kernels are Cameron-Martin spaces of certain Gaussian processes. This paves the way towards uncertainty quantification for signature kernel regression.

Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despite their compelling theoretical guarantee on performance and communication efficiency, this assumption is overly idealistic and oftentimes violated in practice, especially when the algorithm is operated over self-interested clients, who are reluctant to share data without explicit benefits. Negligence of such self-interested behaviors can significantly affect the learning efficiency and even the practical operability of federated bandit learning. In light of this, we aim to spark new insights into this under-explored research area by formally introducing an incentivized communication problem for federated bandits, where the server shall motivate clients to share data by providing incentives. Without loss of generality, we instantiate this bandit problem with the contextual linear setting and propose the first incentivized communication protocol, namely, Inc-FedUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees. Extensive empirical experiments on both synthetic and real-world datasets further validate the effectiveness of the proposed method across various environments.

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism.

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a crucial role in understanding the organization and functioning of complex systems. We begin by introducing the concept of community structure, which refers to the arrangement of vertices into clusters, with strong internal connections and weaker connections between clusters. Then, we provide a thorough exposition of various community detection methods, including a new method designed by us. Additionally, we explore real-world applications of community detection in diverse networks. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. It serves as a valuable resource for researchers and practitioners in multiple disciplines, offering insights into the challenges, methodologies, and applications of community detection in complex networks.

Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles. Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text. Given the surge in development of new LLMs, acquiring labeled training data for supervised detectors is a bottleneck. However, there might be plenty of unlabeled text data available, without information on which generator it came from. In this work we tackle this data problem, in detecting AI-generated news text, and frame the problem as an unsupervised domain adaptation task. Here the domains are the different text generators, i.e. LLMs, and we assume we have access to only the labeled source data and unlabeled target data. We develop a Contrastive Domain Adaptation framework, called ConDA, that blends standard domain adaptation techniques with the representation power of contrastive learning to learn domain invariant representations that are effective for the final unsupervised detection task. Our experiments demonstrate the effectiveness of our framework, resulting in average performance gains of 31.7% from the best performing baselines, and within 0.8% margin of a fully supervised detector. All our code and data is available at https://github.com/AmritaBh/ConDA-gen-text-detection.

Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role. However, the recent development of new non-CL frameworks has achieved comparable or better performance with high improvement potential, prompting researchers to enhance these frameworks further. Assimilating CL into non-CL frameworks has been thought to be beneficial, but empirical evidence indicates no visible improvements. In view of that, this paper proposes a strategy of performing CL along the dimensional direction instead of along the batch direction as done in conventional contrastive learning, named Dimensional Contrastive Learning (DimCL). DimCL aims to enhance the feature diversity, and it can serve as a regularizer to prior SSL frameworks. DimCL has been found to be effective, and the hardness-aware property is identified as a critical reason for its success. Extensive experimental results reveal that assimilating DimCL into SSL frameworks leads to performance improvement by a non-trivial margin on various datasets and backbone architectures.

We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL. We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels. These results open up new possibilities for ICL with privacy protection for a broad range of applications.  ( 2 min )

Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate scale quantum (NISQ) computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions, while having access only to limited training data. Existing generalization analyses, while identifying important general trends and scaling laws, cannot be used to assign reliable and informative "error bars" to the decisions made by quantum models. In this article, we propose a general methodology that can reliably quantify the uncertainty of quantum models, irrespective of the amount of training data, of the number of shots, of the ansatz, of the training algorithm, and of the presence of quantum hardware noise. The approach, which builds on probabilistic conformal prediction, turns an arbitrary, possibly small, number of shots from a pre-trained quantum model into a set prediction, e.g., an interval, that provably contains the true target with any desired coverage level. Experimental results confirm the theoretical calibration guarantees of the proposed framework, referred to as quantum conformal prediction.

Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions. However, it has been challenging to implement in realistic or open-ended environments. A main challenge has been to find the right space of sub-goals over which to instantiate a hierarchy. We present a novel approach where we use data from humans solving these tasks to softly supervise the goal space for a set of long range tasks in a 3D embodied environment. In particular, we use unconstrained natural language to parameterize this space. This has two advantages: first, it is easy to generate this data from naive human participants; second, it is flexible enough to represent a vast range of sub-goals in human-relevant tasks. Our approach outperforms agents that clone expert behavior on these tasks, as well as HRL from scratch without this supervised sub-goal space. Our work presents a novel approach to combining human expert supervision with the benefits and flexibility of reinforcement learning.

Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate functions, referred to as components. However, this assumption fails in ML problems where the output depends on multiple features simultaneously. In these cases, GAMs fail to capture the interaction terms of the underlying function, leading to subpar accuracy. To (partially) address this issue, we propose Regionally Additive Models (RAMs), a novel class of explainable-by-design models. RAMs identify subregions within the feature space where interactions are minimized. Within these regions, it is more accurate to express the output as a sum of univariate functions (components). Consequently, RAMs fit one component per subregion of each feature instead of one component per feature. This approach yields a more expressive model compared to GAMs while retaining interpretability. The RAM framework consists of three steps. Firstly, we train a black-box model. Secondly, using Regional Effect Plots, we identify subregions where the black-box model exhibits near-local additivity. Lastly, we fit a GAM component for each identified subregion. We validate the effectiveness of RAMs through experiments on both synthetic and real-world datasets. The results confirm that RAMs offer improved expressiveness compared to GAMs while maintaining interpretability.

We consider the problem of estimating the learning rate in adaptive methods, such as Adagrad and Adam. We describe two techniques, Prodigy and Resetting, to provably estimate the distance to the solution $D$, which is needed to set the learning rate optimally. Our techniques are modifications of the D-Adaptation method for learning-rate-free learning. Our methods improve upon the convergence rate of D-Adaptation by a factor of $O(\sqrt{\log(D/d_0)})$, where $d_0$ is the initial estimate of $D$. We test our methods on 12 common logistic-regression benchmark datasets, VGG11 and ResNet-50 training on CIFAR10, ViT training on Imagenet, LSTM training on IWSLT14, DLRM training on Criteo dataset, VarNet on Knee MRI dataset, as well as RoBERTa and GPT transformer training on BookWiki. Our experimental results show that our approaches consistently outperform D-Adaptation and reach test accuracy values close to that of hand-tuned Adam.

The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual agents, in multi-agent reinforcement learning settings, discovering collaborative options that can coordinate the behavior of multiple agents and encourage them to visit the under-explored regions of their joint state space has not been considered. In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space. Also, we propose a novel framework to adopt the multi-agent options in the MARL process. In practice, a multi-agent task can usually be divided into some sub-tasks, each of which can be completed by a sub-group of the agents. Therefore, our algorithm framework first leverages an attention mechanism to find collaborative agent sub-groups that would benefit most from coordinated actions. Then, a hierarchical algorithm, namely HA-MSAC, is developed to learn the multi-agent options for each sub-group to complete their sub-tasks first, and then to integrate them through a high-level policy as the solution of the whole task. This hierarchical option construction allows our framework to strike a balance between scalability and effective collaboration among the agents. The evaluation based on multi-agent collaborative tasks shows that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher task rewards.

A hypothesis class admits a sample compression scheme, if for every sample labeled by a hypothesis from the class, it is possible to retain only a small subsample, using which the labels on the entire sample can be inferred. The size of the compression scheme is an upper bound on the size of the subsample produced. Every learnable binary hypothesis class (which must necessarily have finite VC dimension) admits a sample compression scheme of size only a finite function of its VC dimension, independent of the sample size. For multiclass hypothesis classes, the analog of VC dimension is the DS dimension. We show that the analogous statement pertaining to sample compression is not true for multiclass hypothesis classes: every learnable multiclass hypothesis class, which must necessarily have finite DS dimension, does not admit a sample compression scheme of size only a finite function of its DS dimension.

Designing reward functions is a longstanding challenge in reinforcement learning (RL); it requires specialized knowledge or domain data, leading to high costs for development. To address this, we introduce Text2Reward, a data-free framework that automates the generation of dense reward functions based on large language models (LLMs). Given a goal described in natural language, Text2Reward generates dense reward functions as an executable program grounded in a compact representation of the environment. Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback. We evaluate Text2Reward on two robotic manipulation benchmarks (ManiSkill2, MetaWorld) and two locomotion environments of MuJoCo. On 13 of the 17 manipulation tasks, policies trained with generated reward codes achieve similar or better task success rates and convergence speed than expert-written reward codes. For locomotion tasks, our method learns six novel locomotion behaviors with a success rate exceeding 94%. Furthermore, we show that the policies trained in the simulator with our method can be deployed in the real world. Finally, Text2Reward further improves the policies by refining their reward functions with human feedback. Video results are available at https://text-to-reward.github.io

The problem of function approximation by neural dynamical systems has typically been approached in a top-down manner: Any continuous function can be approximated to an arbitrary accuracy by a sufficiently complex model with a given architecture. This can lead to high-complexity controls which are impractical in applications. In this paper, we take the opposite, constructive approach: We impose various structural restrictions on system dynamics and consequently characterize the class of functions that can be realized by such a system. The systems are implemented as a cascade interconnection of a neural stochastic differential equation (Neural SDE), a deterministic dynamical system, and a readout map. Both probabilistic and geometric (Lie-theoretic) methods are used to characterize the classes of functions realized by such systems.

The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly we will examine the three different workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.

This work is devoted to the study of the probability of immunity, i.e. the effect occurs whether exposed or not. We derive necessary and sufficient conditions for non-immunity and $\epsilon$-bounded immunity, i.e. the probability of immunity is zero and $\epsilon$-bounded, respectively. The former allows us to estimate the probability of benefit (i.e., the effect occurs if and only if exposed) from a randomized controlled trial, and the latter allows us to produce bounds of the probability of benefit that are tighter than the existing ones. We also introduce the concept of indirect immunity (i.e., through a mediator) and repeat our previous analysis for it. Finally, we propose a method for sensitivity analysis of the probability of immunity under unmeasured confounding.

Monte Carlo (MC) approximation has been used as the standard computation approach for the Sliced Wasserstein (SW) distance, which has an intractable expectation in its analytical form. However, the MC method is not optimal in terms of minimizing the absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically verify various ways of constructing QMC points sets on the 3D unit-hypersphere, including Gaussian-based mapping, equal area mapping, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimation for stochastic optimization, we extend QSW into Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness to the discussed low-discrepancy sequences. For theoretical properties, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants.  ( 2 min )

Quantization of deep neural networks (DNN) has become a key element in the efforts of embedding such networks on end-user devices. However, current quantization methods usually suffer from costly accuracy degradation. In this paper, we propose a new method for Enhanced Post Training Quantization named EPTQ. The method is based on knowledge distillation with an adaptive weighting of layers. In addition, we introduce a new label-free technique for approximating the Hessian trace of the task loss, named Label-Free Hessian. This technique removes the requirement of a labeled dataset for computing the Hessian. The adaptive knowledge distillation uses the Label-Free Hessian technique to give greater attention to the sensitive parts of the model while performing the optimization. Empirically, by employing EPTQ we achieve state-of-the-art results on a wide variety of models, tasks, and datasets, including ImageNet classification, COCO object detection, and Pascal-VOC for semantic segmentation. We demonstrate the performance and compatibility of EPTQ on an extended set of architectures, including CNNs, Transformers, hybrid, and MLP-only models.  ( 2 min )

This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution. Starting from a simple prior and iteratively updating the two distributions yields a generative procedure similar to the reverse process of diffusion models; however it is conceptually simpler in that no forward process is required. Discrete and continuous-time loss functions are derived for continuous, discretised and discrete data, along with sample generation procedures. Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modelling. The loss function directly optimises data compression and places no restrictions on the network architecture. In our experiments BFNs achieve competitive log-likelihoods for image modelling on dynamically binarized MNIST and CIFAR-10, and outperform all known discrete diffusion models on the text8 character-level language modelling task.

The recommendation of appropriate development pathways, also known as ecological civilization patterns for achieving Sustainable Development Goals (namely, sustainable development patterns), are of utmost importance for promoting ecological, economic, social, and resource sustainability in a specific region. To achieve this, the recommendation process must carefully consider the region's natural, environmental, resource, and economic characteristics. However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns. To overcome these challenges, this paper proposes a method called User Graph after Pruning and Intent Graph (UGPIG). Firstly, we utilize the high-density linking capability of the pruned User Graph to address the issue of spatial heterogeneity neglect in recommendation algorithms. Secondly, we construct an Intent Graph by incorporating the intent network, which captures the preferences for attributes including environmental elements of target regions. This approach effectively alleviates the problem of sparse historical interaction data in the region. Through extensive experiments, we demonstrate that UGPIG outperforms state-of-the-art recommendation algorithms like KGCN, KGAT, and KGIN in sustainable development pattern recommendations, with a maximum improvement of 9.61% in Top-3 recommendation performance.  ( 2 min )

The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.

Motivated by applications in queueing theory, we consider a stochastic control problem whose state space is the $d$-dimensional positive orthant. The controlled process $Z$ evolves as a reflected Brownian motion whose covariance matrix is exogenously specified, as are its directions of reflection from the orthant's boundary surfaces. A system manager chooses a drift vector $\theta(t)$ at each time $t$ based on the history of $Z$, and the cost rate at time $t$ depends on both $Z(t)$ and $\theta(t)$. In our initial problem formulation, the objective is to minimize expected discounted cost over an infinite planning horizon, after which we treat the corresponding ergodic control problem. Extending earlier work by Han et al. (Proceedings of the National Academy of Sciences, 2018, 8505-8510), we develop and illustrate a simulation-based computational method that relies heavily on deep neural network technology. For test problems studied thus far, our method is accurate to within a fraction of one percent, and is computationally feasible in dimensions up to at least $d=30$.

We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at the expense of loss in recommendation quality. We show that incorporating public item features during training can help mitigate this loss in quality. We propose a general approach based on collective matrix factorization (CMF), that works by simultaneously factorizing two matrices: the user feedback matrix (representing sensitive data) and an item feature matrix that encodes publicly available (non-sensitive) item information. The method is conceptually simple, easy to tune, and highly scalable. It can be applied to different types of public item data, including: (1) categorical item features; (2) item-item similarities learned from public sources; and (3) publicly available user feedback. Furthermore, these data modalities can be collectively utilized to fully leverage public data. Evaluating our method on a standard DP recommendation benchmark, we find that using public item features significantly narrows the quality gap between private models and their non-private counterparts. As privacy constraints become more stringent, models rely more heavily on public side features for recommendation. This results in a smooth transition from collaborative filtering to item-based contextual recommendations.

This paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs). Instruction tuning refers to the process of further training LLMs on a dataset consisting of \textsc{(instruction, output)} pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. In this work, we make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications, along with an analysis on aspects that influence the outcome of IT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research.

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. It is shown that this solution can be adapted for software calibration of sensors, implementation of expert-based adaptation, and federated learning methods. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. The results show an improvement for both the simulation and the experiments with real data.

Music streaming services heavily rely on their recommendation engines to continuously provide content to their consumers. Sequential recommendation consequently has seen considerable attention in current literature, where state of the art approaches focus on self-attentive models leveraging contextual information such as long and short-term user history and item features; however, most of these studies focus on long-form content domains (retail, movie, etc.) rather than short-form, such as music. Additionally, many do not explore incorporating negative session-level feedback during training. In this study, we investigate the use of transformer-based self-attentive architectures to learn implicit session-level information for sequential music recommendation. We additionally propose a contrastive learning task to incorporate negative feedback (e.g skipped tracks) to promote positive hits and penalize negative hits. This task is formulated as a simple loss term that can be incorporated into a variety of deep learning architectures for sequential recommendation. Our experiments show that this results in consistent performance gains over the baseline architectures ignoring negative user feedback.

A sequential pattern with negation, or negative sequential pattern, takes the form of a sequential pattern for which the negation symbol may be used in front of some of the pattern's itemsets. Intuitively, such a pattern occurs in a sequence if negated itemsets are absent in the sequence. Recent work has shown that different semantics can be attributed to these pattern forms, and that state-of-the-art algorithms do not extract the same sets of patterns. This raises the important question of the interpretability of sequential pattern with negation. In this study, our focus is on exploring how potential users perceive negation in sequential patterns. Our aim is to determine whether specific semantics are more "intuitive" than others and whether these align with the semantics employed by one or more state-of-the-art algorithms. To achieve this, we designed a questionnaire to reveal the semantics' intuition of each user. This article presents both the design of the questionnaire and an in-depth analysis of the 124 responses obtained. The outcomes indicate that two of the semantics are predominantly intuitive; however, neither of them aligns with the semantics of the primary state-of-the-art algorithms. As a result, we provide recommendations to account for this disparity in the conclusions drawn.

Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to approximate specific closed-form statistical features, setting a promising foundation for deep learning architectures. While the applicability of FINs has been chiefly tested in biomedical domains, this study extends its exploration into other time series datasets. Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection. For the Bitcoin price prediction, models embedded with FINs reduced the root mean square error by around 1000 compared to the baseline. In the speech emotion recognition task, the FIN-augmented model increased classification accuracy by over 3 percent. Lastly, in the CNP detection experiment, an improvement of about 7 percent was observed compared to established classifiers. These findings validate the broad utility and potency of FINs in diverse applications.  ( 2 min )

Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.  ( 2 min )

Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However, due to the unsolved adversarial robustness problem of vision models, MLLMs can have more severe safety and security risks by introducing the vision inputs. In this work, we study the adversarial robustness of Google's Bard, a competitive chatbot to ChatGPT that released its multimodal capability recently, to better understand the vulnerabilities of commercial MLLMs. By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability. We show that the adversarial examples can also attack other MLLMs, e.g., a 26% attack success rate against Bing Chat and a 86% attack success rate against ERNIE bot. Moreover, we identify two defense mechanisms of Bard, including face detection and toxicity detection of images. We design corresponding attacks to evade these defenses, demonstrating that the current defenses of Bard are also vulnerable. We hope this work can deepen our understanding on the robustness of MLLMs and facilitate future research on defenses. Our code is available at https://github.com/thu-ml/Attack-Bard.  ( 2 min )

Self-supervised representation learning has seen remarkable progress in the last few years, with some of the recent methods being able to learn useful image representations without labels. These methods are trained using backpropagation, the de facto standard. Recently, Geoffrey Hinton proposed the forward-forward algorithm as an alternative training method. It utilizes two forward passes and a separate loss function for each layer to train the network without backpropagation. In this study, for the first time, we study the performance of forward-forward vs. backpropagation for self-supervised representation learning and provide insights into the learned representation spaces. Our benchmark employs four standard datasets, namely MNIST, F-MNIST, SVHN and CIFAR-10, and three commonly used self-supervised representation learning techniques, namely rotation, flip and jigsaw. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-)supervised training, the transfer performance is significantly lagging behind in all the studied settings. This may be caused by a combination of factors, including having a loss function for each layer and the way the supervised training is realized in the forward-forward paradigm. In comparison to backpropagation, the forward-forward algorithm focuses more on the boundaries and drops part of the information unnecessary for making decisions which harms the representation learning goal. Further investigation and research are necessary to stabilize the forward-forward strategy for self-supervised learning, to work beyond the datasets and configurations demonstrated by Geoffrey Hinton.  ( 2 min )

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.  ( 2 min )

Many applications of cross-modal music retrieval are related to connecting sheet music images to audio recordings. A typical and recent approach to this is to learn, via deep neural networks, a joint embedding space that correlates short fixed-size snippets of audio and sheet music by means of an appropriate similarity structure. However, two challenges that arise out of this strategy are the requirement of strongly aligned data to train the networks, and the inherent discrepancies of musical content between audio and sheet music snippets caused by local and global tempo differences. In this paper, we address these two shortcomings by designing a cross-modal recurrent network that learns joint embeddings that can summarize longer passages of corresponding audio and sheet music. The benefits of our method are that it only requires weakly aligned audio-sheet music pairs, as well as that the recurrent network handles the non-linearities caused by tempo variations between audio and sheet music. We conduct a number of experiments on synthetic and real piano data and scores, showing that our proposed recurrent method leads to more accurate retrieval in all possible configurations.  ( 2 min )

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs.io/en/latest.  ( 2 min )

Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activation compression (EXACT) which demonstrated drastic reduction in memory consumption by performing quantization of the intermediate activation maps down to using INT2 precision. They showed little to no reduction in performance while achieving large reductions in GPU memory consumption. In this work, we present an improvement to the EXACT strategy by using block-wise quantization of the intermediate activation maps. We experimentally analyze different block sizes and show further reduction in memory consumption (>15%), and runtime speedup per epoch (about 5%) even when performing extreme extents of quantization with similar performance trade-offs as with the original EXACT. Further, we present a correction to the assumptions on the distribution of intermediate activation maps in EXACT (assumed to be uniform) and show improved variance estimations of the quantization and dequantization steps.  ( 2 min )

Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1% range-normalised and 1 deg, respectively. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure.  ( 2 min )

In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist popularity. We find that both algorithms improve recommendation performance for more popular artists and, as such, exhibit popularity degradation bias. While both algorithms produce a similar level of performance for more popular artists, Mult-VAE shows better relative performance for less popular artists. This suggests that this algorithm should be preferred for local (long-tail) music artist recommendation.  ( 2 min )

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The fusionACS project seeks to integrate data from multiple U.S. household surveys by statistically "fusing" variables from "donor" surveys onto American Community Survey (ACS) microdata. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The presented data comprise the fusion onto the ACS of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Transportation Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015-2019. The underlying statistical techniques are included in an open-source $R$ package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.  ( 2 min )

While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions. In the presence of distribution shifts, fair models may behave unfairly on test data. There have been some developments for fair learning robust to distribution shifts to address this shortcoming. However, most proposed solutions are based on the assumption of having access to the causal graph describing the interaction of different features. Moreover, existing algorithms require full access to data and cannot be used when small batches are used (stochastic/batch implementation). This paper proposes the first stochastic distributionally robust fairness framework with convergence guarantees that do not require knowledge of the causal graph. More specifically, we formulate the fair inference in the presence of the distribution shift as a distributionally robust optimization problem under $L_p$ norm uncertainty sets with respect to the Exponential Renyi Mutual Information (ERMI) as the measure of fairness violation. We then discuss how the proposed method can be implemented in a stochastic fashion. We have evaluated the presented framework's performance and efficiency through extensive experiments on real datasets consisting of distribution shifts.  ( 2 min )

Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and revenue while maintaining a satisfactory user experience. This requires the optimization of conflicting objectives, such as user satisfaction and ads revenue. Traditional approaches to ad-load balancing rely on static allocation policies, which fail to adapt to changing user preferences and contextual factors. In this paper, we present an approach that leverages off-policy learning and evaluation from logged bandit feedback. We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue. We emphasize the nuances that arise due to user heterogeneity and the dependence on the user's position within a session. Based on this analysis, we define the problem as determining the optimal ad-load for a particular feed fetch. To tackle this problem, we propose an off-policy learning framework that leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and Doubly Robust (DR) to learn and estimate the policy values using offline collected stochastic data. We present insights from online A/B experiments deployed at scale across over 80 million users generating over 200 million sessions, where we find statistically significant improvements in both user satisfaction metrics and ads revenue for the platform.  ( 2 min )

With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted to preserve privacy in recommender systems. However, existing differentially private recommender systems only consider static and independent interactions, so they cannot apply to sequential recommendation where behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.  ( 2 min )

Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper proposes a framework for the generative design of structural components. Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions. One of the distinct advantages our approach offers over other generative approaches, such as generative adversarial networks (GANs), is that it permits the editing of existing designs. We train our model using a dataset of geometries obtained from structural topology optimization utilizing the SIMP algorithm. Consequently, our framework generates inherently near-optimal designs. Our work presents quantitative results that support the structural performance of the generated designs and the variability in potential candidate designs. Furthermore, we provide evidence of the scalability of our framework by operating over voxel domains with resolutions varying from $32^3$ to $128^3$. Our framework can be used as a starting point for generating novel near-optimal designs similar to topology-optimized designs.  ( 2 min )

Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activation compression (EXACT) which demonstrated drastic reduction in memory consumption by performing quantization of the intermediate activation maps down to using INT2 precision. They showed little to no reduction in performance while achieving large reductions in GPU memory consumption. In this work, we present an improvement to the EXACT strategy by using block-wise quantization of the intermediate activation maps. We experimentally analyze different block sizes and show further reduction in memory consumption (>15%), and runtime speedup per epoch (about 5%) even when performing extreme extents of quantization with similar performance trade-offs as with the original EXACT. Further, we present a correction to the assumptions on the distribution of intermediate activation maps in EXACT (assumed to be uniform) and show improved variance estimations of the quantization and dequantization steps.  ( 2 min )

This work is devoted to the study of the probability of immunity, i.e. the effect occurs whether exposed or not. We derive necessary and sufficient conditions for non-immunity and $\epsilon$-bounded immunity, i.e. the probability of immunity is zero and $\epsilon$-bounded, respectively. The former allows us to estimate the probability of benefit (i.e., the effect occurs if and only if exposed) from a randomized controlled trial, and the latter allows us to produce bounds of the probability of benefit that are tighter than the existing ones. We also introduce the concept of indirect immunity (i.e., through a mediator) and repeat our previous analysis for it. Finally, we propose a method for sensitivity analysis of the probability of immunity under unmeasured confounding.

Recently, studies on machine learning have focused on methods that use symmetry implicit in a specific manifold as an inductive bias. Grassmann manifolds provide the ability to handle fundamental shapes represented as shape spaces, enabling stable shape analysis. In this paper, we present a novel approach in which we establish the theoretical foundations for learning distributions on the Grassmann manifold via continuous normalization flows, with the explicit goal of generating stable shapes. Our approach facilitates more robust generation by effectively eliminating the influence of extraneous transformations, such as rotations and inversions, through learning and generating within a Grassmann manifolds designed to accommodate the essential shape information of the object. The experimental results indicated that the proposed method can generate high-quality samples by capturing the data structure. Furthermore, the proposed method significantly outperformed state-of-the-art methods in terms of the log-likelihood or evidence lower bound. The results obtained are expected to stimulate further research in this field, leading to advances for stable shape generation and analysis.

Sequential change detection is a classical problem with a variety of applications. However, the majority of prior work has been parametric, for example, focusing on exponential families. We develop a fundamentally new and general framework for sequential change detection when the pre- and post-change distributions are nonparametrically specified (and thus composite). Our procedures come with clean, nonasymptotic bounds on the average run length (frequency of false alarms). In certain nonparametric cases (like sub-Gaussian or sub-exponential), we also provide near-optimal bounds on the detection delay following a changepoint. The primary technical tool that we introduce is called an \emph{e-detector}, which is composed of sums of e-processes -- a fundamental generalization of nonnegative supermartingales -- that are started at consecutive times. We first introduce simple Shiryaev-Roberts and CUSUM-style e-detectors, and then show how to design their mixtures in order to achieve both statistical and computational efficiency. Our e-detector framework can be instantiated to recover classical likelihood-based procedures for parametric problems, as well as yielding the first change detection method for many nonparametric problems. As a running example, we tackle the problem of detecting changes in the mean of a bounded random variable without i.i.d. assumptions, with an application to tracking the performance of a basketball team over multiple seasons.

Thanks to their dependency structure, non-parametric Hidden Markov Models (HMMs) are able to handle model-based clustering without specifying group distributions. The aim of this work is to study the Bayes risk of clustering when using HMMs and to propose associated clustering procedures. We first give a result linking the Bayes risk of classification and the Bayes risk of clustering, which we use to identify the key quantity determining the difficulty of the clustering task. We also give a proof of this result in the i.i.d. framework, which might be of independent interest. Then we study the excess risk of the plugin classifier. All these results are shown to remain valid in the online setting where observations are clustered sequentially. Simulations illustrate our findings.

A hypothesis class admits a sample compression scheme, if for every sample labeled by a hypothesis from the class, it is possible to retain only a small subsample, using which the labels on the entire sample can be inferred. The size of the compression scheme is an upper bound on the size of the subsample produced. Every learnable binary hypothesis class (which must necessarily have finite VC dimension) admits a sample compression scheme of size only a finite function of its VC dimension, independent of the sample size. For multiclass hypothesis classes, the analog of VC dimension is the DS dimension. We show that the analogous statement pertaining to sample compression is not true for multiclass hypothesis classes: every learnable multiclass hypothesis class, which must necessarily have finite DS dimension, does not admit a sample compression scheme of size only a finite function of its DS dimension.  ( 2 min )

Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing. As in classical learning, quantum learning problems involve settings in which the mechanism generating the data is unknown, and the main goal of a learning algorithm is to ensure satisfactory accuracy levels when only given access to data and, possibly, side information such as expert knowledge. This article reviews the complexity of quantum learning using information-theoretic techniques by focusing on data complexity, copy complexity, and model complexity. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. For example, in a quantum system, unlike in classical machine learning, it is generally not possible to evaluate the training loss simultaneously on multiple hypotheses using the same quantum data. To make the paper self-contained and approachable by different research communities, we provide extensive background material on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, we highlight the differences between quantum and classical learning by addressing both supervised and unsupervised learning, and we provide extensive pointers to the literature.

Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function $h$, and possibly non-Lipschitz. We analyze the regret of online mirror descent with $h$. Then, based on the result, we prove the following in a unified manner. Denote by $T$ the time horizon and $d$ the parameter dimension. 1. For online portfolio selection, the regret of $\widetilde{\text{EG}}$, a variant of exponentiated gradient due to Helmbold et al., is $\tilde{O} ( T^{2/3} d^{1/3} )$ when $T > 4 d / \log d$. This improves on the original $\tilde{O} ( T^{3/4} d^{1/2} )$ regret bound for $\widetilde{\text{EG}}$. 2. For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$. The regret bound is the same as that of Soft-Bayes due to Orseau et al. up to logarithmic terms. 3. For online learning quantum states with the logarithmic loss, the regret of online mirror descent with the log-determinant function is also $\tilde{O} ( \sqrt{T d} )$. Its per-iteration time is shorter than all existing algorithms we know.

Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees). Furthermore, our algorithms adapt to model misspecification and extend to the continuous arm settings. These advantages come from constructing and relying on "conformal arm sets" (CASs), which provide a set of arms at every context that encompass the context-specific optimal arm with some probability across the context distribution. Our positive results on simple and cumulative regret guarantees are contrasted by a negative result, which shows that an algorithm can't achieve instance-dependent simple regret guarantees while simultaneously achieving minimax optimal cumulative regret guarantees.  ( 2 min )

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.

Structure learning is the crux of causal inference. Notably, causal discovery (CD) algorithms are brittle when data is scarce, possibly inferring imprecise causal relations that contradict expert knowledge -- especially when considering latent confounders. To aggravate the issue, most CD methods do not provide uncertainty estimates, making it hard for users to interpret results and improve the inference process. Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD. To solve these issues, we start by proposing to sample (causal) ancestral graphs proportionally to a belief distribution based on a score function, such as the Bayesian information criterion (BIC), using generative flow networks. Then, we leverage the diversity in candidate graphs and introduce an optimal experimental design to iteratively probe the expert about the relations among variables, effectively reducing the uncertainty of our belief over ancestral graphs. Finally, we update our samples to incorporate human feedback via importance sampling. Importantly, our method does not require causal sufficiency (i.e., unobserved confounders may exist). Experiments with synthetic observational data show that our method can accurately sample from distributions over ancestral graphs and that we can greatly improve inference quality with human aid.  ( 2 min )

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs.io/en/latest.

Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications. The state-of-the-art -- in Bayesian sparsification of deep neural networks -- combines structural shrinkage priors on model weights with an approximate inference scheme based on black-box stochastic variational inference. However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates. In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights. As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model. Our comparative study highlights the computational efficiency and the pruning rate of the BMR method relative to the established stochastic variational inference (SVI) scheme, when applied to the full hierarchical generative model. We illustrate the potential of BMR to prune model parameters across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers.

While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions. In the presence of distribution shifts, fair models may behave unfairly on test data. There have been some developments for fair learning robust to distribution shifts to address this shortcoming. However, most proposed solutions are based on the assumption of having access to the causal graph describing the interaction of different features. Moreover, existing algorithms require full access to data and cannot be used when small batches are used (stochastic/batch implementation). This paper proposes the first stochastic distributionally robust fairness framework with convergence guarantees that do not require knowledge of the causal graph. More specifically, we formulate the fair inference in the presence of the distribution shift as a distributionally robust optimization problem under $L_p$ norm uncertainty sets with respect to the Exponential Renyi Mutual Information (ERMI) as the measure of fairness violation. We then discuss how the proposed method can be implemented in a stochastic fashion. We have evaluated the presented framework's performance and efficiency through extensive experiments on real datasets consisting of distribution shifts.

We consider the problem of estimating the learning rate in adaptive methods, such as Adagrad and Adam. We describe two techniques, Prodigy and Resetting, to provably estimate the distance to the solution $D$, which is needed to set the learning rate optimally. Our techniques are modifications of the D-Adaptation method for learning-rate-free learning. Our methods improve upon the convergence rate of D-Adaptation by a factor of $O(\sqrt{\log(D/d_0)})$, where $d_0$ is the initial estimate of $D$. We test our methods on 12 common logistic-regression benchmark datasets, VGG11 and ResNet-50 training on CIFAR10, ViT training on Imagenet, LSTM training on IWSLT14, DLRM training on Criteo dataset, VarNet on Knee MRI dataset, as well as RoBERTa and GPT transformer training on BookWiki. Our experimental results show that our approaches consistently outperform D-Adaptation and reach test accuracy values close to that of hand-tuned Adam.  ( 2 min )

Monte Carlo (MC) approximation has been used as the standard computation approach for the Sliced Wasserstein (SW) distance, which has an intractable expectation in its analytical form. However, the MC method is not optimal in terms of minimizing the absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically verify various ways of constructing QMC points sets on the 3D unit-hypersphere, including Gaussian-based mapping, equal area mapping, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimation for stochastic optimization, we extend QSW into Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness to the discussed low-discrepancy sequences. For theoretical properties, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants.  ( 2 min )

We demonstrate the first algorithms for the problem of regression for generalized linear models (GLMs) in the presence of additive oblivious noise. We assume we have sample access to examples $(x, y)$ where $y$ is a noisy measurement of $g(w^* \cdot x)$. In particular, \new{the noisy labels are of the form} $y = g(w^* \cdot x) + \xi + \epsilon$, where $\xi$ is the oblivious noise drawn independently of $x$ \new{and satisfies} $\Pr[\xi = 0] \geq o(1)$, and $\epsilon \sim \mathcal N(0, \sigma^2)$. Our goal is to accurately recover a \new{parameter vector $w$ such that the} function $g(w \cdot x)$ \new{has} arbitrarily small error when compared to the true values $g(w^* \cdot x)$, rather than the noisy measurements $y$. We present an algorithm that tackles \new{this} problem in its most general distribution-independent setting, where the solution may not \new{even} be identifiable. \new{Our} algorithm returns \new{an accurate estimate of} the solution if it is identifiable, and otherwise returns a small list of candidates, one of which is close to the true solution. Furthermore, we \new{provide} a necessary and sufficient condition for identifiability, which holds in broad settings. \new{Specifically,} the problem is identifiable when the quantile at which $\xi + \epsilon = 0$ is known, or when the family of hypotheses does not contain candidates that are nearly equal to a translated $g(w^* \cdot x) + A$ for some real number $A$, while also having large error when compared to $g(w^* \cdot x)$. This is the first \new{algorithmic} result for GLM regression \new{with oblivious noise} which can handle more than half the samples being arbitrarily corrupted. Prior work focused largely on the setting of linear regression, and gave algorithms under restrictive assumptions.  ( 3 min )

Modern deep networks are trained with stochastic gradient descent (SGD) whose key parameters are the number of data considered at each step or batch size $B$, and the step size or learning rate $\eta$. For small $B$ and large $\eta$, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the `temperature' $T\equiv \eta/B$. Yet this description is observed to break down for sufficiently large batches $B\geq B^*$, or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here we resolve these questions for a teacher-student perceptron classification model, and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the $B$-$\eta$ plane that separates three dynamical phases: $\textit{(i)}$ a noise-dominated SGD governed by temperature, $\textit{(ii)}$ a large-first-step-dominated SGD and $\textit{(iii)}$ GD. These different phases also corresponds to different regimes of generalization error. Remarkably, our analysis reveals that the batch size $B^*$ separating regimes $\textit{(i)}$ and $\textit{(ii)}$ scale with the size $P$ of the training set, with an exponent that characterizes the hardness of the classification problem.  ( 2 min )

Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate functions, referred to as components. However, this assumption fails in ML problems where the output depends on multiple features simultaneously. In these cases, GAMs fail to capture the interaction terms of the underlying function, leading to subpar accuracy. To (partially) address this issue, we propose Regionally Additive Models (RAMs), a novel class of explainable-by-design models. RAMs identify subregions within the feature space where interactions are minimized. Within these regions, it is more accurate to express the output as a sum of univariate functions (components). Consequently, RAMs fit one component per subregion of each feature instead of one component per feature. This approach yields a more expressive model compared to GAMs while retaining interpretability. The RAM framework consists of three steps. Firstly, we train a black-box model. Secondly, using Regional Effect Plots, we identify subregions where the black-box model exhibits near-local additivity. Lastly, we fit a GAM component for each identified subregion. We validate the effectiveness of RAMs through experiments on both synthetic and real-world datasets. The results confirm that RAMs offer improved expressiveness compared to GAMs while maintaining interpretability.  ( 2 min )

We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family as hidden layer maps and a non-linear activation function applied to each hidden layer. Relying on Stone-Weierstrass theorems on weighted spaces, we can prove a global universal approximation result for generalizations of continuous functions going beyond the usual approximation on compact sets. This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks. As a further application of the weighted Stone-Weierstrass theorem we prove a global universal approximation result for linear functions of the signature. We also introduce the viewpoint of Gaussian process regression in this setting and show that the reproducing kernel Hilbert space of the signature kernels are Cameron-Martin spaces of certain Gaussian processes. This paves the way towards uncertainty quantification for signature kernel regression.  ( 2 min )

Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to approximate specific closed-form statistical features, setting a promising foundation for deep learning architectures. While the applicability of FINs has been chiefly tested in biomedical domains, this study extends its exploration into other time series datasets. Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection. For the Bitcoin price prediction, models embedded with FINs reduced the root mean square error by around 1000 compared to the baseline. In the speech emotion recognition task, the FIN-augmented model increased classification accuracy by over 3 percent. Lastly, in the CNP detection experiment, an improvement of about 7 percent was observed compared to established classifiers. These findings validate the broad utility and potency of FINs in diverse applications.  ( 2 min )

submitted by /u/glenniszen [link] [comments]  ( 8 min )

So I created a voice profile based on recordings of my own voice over on voice.ai. The audio was crisp, loud and clear. I only added about 16 minutes if it though. (A minimum of 15 minutes is recommended) When I take a prerecorded voice clip and feed it through the voice profile I created, it’s just silent for all 15 seconds that it grants you before you upgrade. Then it has the little 2 second audio watermark at the end, where it says “voice AI,” and that I can hear perfectly fine! I’ve searched all over for others who might have had this problem, but it seems like the main problem people have reported difficulties with audio are/were tend to center around the live mode failing to function properly. Has anyone had this issue before? Should I add more audio to the voice profile to make it more well rounded perhaps? If so, how much audio do you typically need to create a solid custom voice profile? Should I just update to the paid version? I don’t really think that would fix it, and I wanted to wait to upgrade until I had some proof that it worked, yanno? I’m not sure what to do… Any ideas? submitted by /u/WhenTheFoxGRINS [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

Founders and venture capitalists who flocked to artificial-intelligence startups are learning that turning the chatbot buzz into successful businesses is harder than it seems. Source https://www.wsj.com/tech/ai/ai-startup-buzz-is-facing-a-reality-check-e34babfe submitted by /u/NuseAI [link] [comments]  ( 8 min )

News provided by aibrews.com Genmo releases a new text-to-video model: Genmo Replay v0.1, which generate high-quality videos from text without the need for advanced prompt engineering. Genmo is available for free to create AI videos [Details | Genmo Replay] . OpenAI unveils DALL·E 3 - a major update to the text-to-image model, which will be integrated in ChatGPT. It will be available to ChatGPT Plus and Enterprise users in October, via the API and in Labs later this fall. Creators can now also opt their images out from future training [Details]. Toyota Research Institute has developed a technique, powered by generative AI, that enables teaching robots new manipulation abilities in a single afternoon. Using the same robot, same code, and same setup, TRI taught over 60 different dextero…  ( 11 min )

As AI models get bigger, training them requires more and more computing power. Researchers are looking for ways to train these large AI models without needing Google-scale resources. A new paper proposes LongLoRA, a fine-tuning approach that can extend LLaMA2 7B to 100k context length and 70B model to 32k context length on a single 8× A100 machine. Here are my highlights from the paper: Big one of course: LongLoRA efficiently fine-tunes large AI models on longer texts Key points: Approximates standard attention via "shift short attention" during training Tuning only a subset of weights (LoRA) plus some embeddings & norms Fine-tuned 7B parameter model on 100k tokens with 1 machine Way lower training cost than full fine-tuning for large contexts Close to full fine-tuning performance The core insight is that an approximation of full attention enables efficient training while retaining standard attention for final inference. Combined with selective weight tuning, this really reduces compute needs. I think this demonstrates the potential to train more capable AI without unreasonable resources. Efficient training techniques = more powerful LLMs for the same resources. Full summary here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

It's currently on the 4th place after marketing, consulting, and accounting. And is mostly used in software testing field to: achieve more accurate results have larger test coverage receive low learning curve get faster QA completion I was actually quite surprised when I read, I thought that information technology would at least be in the TOP-3 industries. Do you think it can happen by the end of this year? submitted by /u/unbalanced_mind [link] [comments]  ( 9 min )

submitted by /u/Tao_Dragon [link] [comments]  ( 8 min )

hello everyone there was a voice-guided navigation system for GTA 4 with a female voice, I would love to use that as my Google Maps and Waze navigation voice I tried looking for anything online but the mods available are of very inferior quality and from a few years ago which won't work properly with Google Maps as a lot of the voice commands are missing the sample for the voice can be found in these 2 YouTube videos first sample 11 minutes repeated statements 2nd sample 1 minute without repeated statements Using Adobe podcast I can remove a lot of the background noise The problem is I don't know much about sound tuning so using whatever free ai I could find on the internet the voice is not being cloned properly it is sounding mechanical or just like google ttss i have a decen…  ( 10 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

submitted by /u/Vegetable_Tutor8245 [link] [comments]  ( 8 min )

Here is a draft speech focusing on the potential for AI to help create a more just and peaceful world by 2030: My fellow citizens, We stand today on the cusp of a new era. The rapid development of artificial intelligence technology presents us with an unparalleled opportunity to remake our world for the better. Though this technology poses risks, I believe that if guided responsibly, AI can help us build a society of prosperity, equity, and justice. Over the past decade, we have already seen glimpses of AI's vast potential for good. Algorithms are helping doctors diagnose diseases more accurately. Smart systems are optimizing transportation networks and reducing traffic accidents. Machine learning is enabling more efficient energy use and sustainable agriculture. But this is only the…  ( 10 min )

Seems like a strong contender in this space, plus comercial use: https://www.splashmusic.com/music-generation submitted by /u/No-Reference8836 [link] [comments]  ( 8 min )

Hello Reddit! I'm Daria from the Cerebras ML team, and I've got some thrilling updates to share with you! 🚀 We've recently published our BTLM-3B-8K paper on arXiv, and I’m excited to share that I am one of the authors! Check it out here: https://arxiv.org/abs/2309.11568 It distills our recipe for training SOTA LLMs: An extensively deduplicated dataset: SlimPajama Hyperparameter search using muP Variable sequence length training + ALiBi Aggressive LR decay Our BTLM-3B-8K not only sets a new standard for 3B parameter models but also frequently surpasses the performance of 7B models! In the paper, we meticulously outline how we developed the BTLM model and conducted a comprehensive analysis of its performance on 22 validation benchmarks. These benchmarks span a range of capabilities including common sense reasoning, world knowledge, reading comprehension, code generation, long sequence interpolation/extrapolation, bias, toxicity, and misinformation. For those eager to dive in, we've made our SlimPajama dataset and the BTLM-3B-8K model available on Hugging Face: https://huggingface.co/cerebras 🎉 Feel free to delve into the details, explore the dataset and model, and let us know your thoughts, insights, or questions! We're here to discuss and excited to hear your feedback. Happy exploring! 🚀 submitted by /u/daria-sobol [link] [comments]  ( 9 min )

Hello all, I am re-reading the LoRA paper (https://arxiv.org/abs/2106.09685) to get a deeper understanding of some of the analysis the authors perform at the end and saw this line Note that the relationship between model size and the optimal rank for adaptation is still an open question. Does anybody know of any resources out there that looked into this question, given that LoRA has been around for a little bit now? Perhaps someone has performed similar subspace overlap / optimal "r" value studies on some of the LLMs that fall in-between GPT2 and 3, i.e. some of the ~7B, ~15B, ~40B and ~70B models? submitted by /u/lightSpeedBrick [link] [comments]  ( 9 min )

If censorship isn’t an issue for me, when there’s an instruct version of an LLM, is it generally always better to use the instruct version than the non-instruct version (because instruct versions tend to hallucinate less)? Apart from censorship and hallucinations, are there any other pros and cons between intrust vs. non-instruct version? submitted by /u/--leockl-- [link] [comments]  ( 9 min )

This new paper from Shehzaad Dhuliawala et al. (2023) introduces a combination of prompting and consistency checks made by the LLM itself. Implementing this technique actually made me like gpt-3.5 again ! I wrote a tutorial on how to actually implement this method : https://advanced-stack.com/resources/how-to-reduce-hallucinations-using-chain-of-verification-cov-in-large-language-models.html Let me know if you find it useful ​ submitted by /u/Fluid-Age-9266 [link] [comments]  ( 9 min )

Hi guys, a final year CS student here. I want to create a portfolio to showcase my skills in ML and DL. I have knowledge in docker and have access to google cloud platform to deploy. Now, I am unable to find any project that stands out. Could u suggest something that I could learn from as well as looks pretty on my cv? submitted by /u/Virtual_Heron_7417 [link] [comments]  ( 9 min )

Hey everyone, I've been diving deep into the world of neural networks, and recently, I've been particularly intrigued by the dynamicity of attention head parameters (weights). These weights play a crucial role in transformers, and understanding how they change during training and inference can provide valuable insights into model behavior. The question is, what does dynamic mean in this context? Is it input-adaptive? Do weights change at inference time according to inputs? I have a hard time understanding this concept, for me, weights are static and pre-established at training time. submitted by /u/assalas23 [link] [comments]  ( 9 min )

Hey everyone, I've been diving deep into the world of neural networks, and recently, I've been particularly intrigued by the dynamicity of attention head parameters (weights). These weights play a crucial role in transformers, and understanding how they change during training and inference can provide valuable insights into model behavior. The question is, what does dynamic mean in this context? Is it input-adaptive? Do weights change at inference time according to inputs? I have a hard time understanding this concept, for me, weights are static and pre-established at training time. submitted by /u/assalas23 [link] [comments]  ( 9 min )

Hello guys! ​ Are you intrigued by the world of Artificial Intelligence? I am conducting a brief and insightful survey on AI for academic research purposes. It'll take just 2 minutes of your time, and your valuable insights will contribute to our understanding of AI trends and perspectives. Whether you're a tech enthusiast, a data wizard, or just curious about the future of AI, your input is incredibly valuable! ​ Click the link below to share your thoughts and help to understand this new technology: ​ https://forms.gle/7fbbkc1f2iBPXHJV7 ​ Thank you in advance for being a part of this exciting AI study! #AIResearch #AI #Artificial Intelligence #SurveyTime submitted by /u/JukeboxNV [link] [comments]  ( 9 min )

As AI models get bigger, training them requires more and more computing power. Researchers are looking for ways to train these large AI models without needing Google-scale resources. A new paper proposes LongLoRA, a fine-tuning approach that can extend LLaMA2 7B to 100k context length and 70B model to 32k context length on a single 8× A100 machine. Here are my highlights from the paper: Big one of course: LongLoRA efficiently fine-tunes large AI models on longer texts Key points: Approximates standard attention via "shift short attention" during training Tuning only a subset of weights (LoRA) plus some embeddings & norms Fine-tuned 7B parameter model on 100k tokens with 1 machine Way lower training cost than full fine-tuning for large contexts Close to full fine-tuning performance The core insight is that an approximation of full attention enables efficient training while retaining standard attention for final inference. Combined with selective weight tuning, this really reduces compute needs. I think this demonstrates the potential to train more capable AI without unreasonable resources. Efficient training techniques = more powerful LLMs for the same resources. Full summary here. Arxiv paper: https://arxiv.org/pdf/2309.12307.pdf submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hello, looking for job opportunities as a data scientist or machine learning engineer. Any help would be appreciated. Thanks!! submitted by /u/ArachnidFun2671 [link] [comments]  ( 8 min )

Denoising diffusion probabilistic models are becoming the leading paradigm of generative modeling for many important data modalities. TabDDPM is a diffusion model that can be universally applied to any tabular dataset and handles any type of feature. Blog post link. submitted by /u/metkere [link] [comments]  ( 9 min )

Context of the problem: I have the following entities: stations, programs, project manager, days, and time slots. The goal of the problem is to decide what program with who project manager to assign on what day at what time slot for each station. Some notes: Each program has its own duration. Say Program A last for 50 minutes, Program B is 30 minutes, etc. Each project manager has qualifications on what program he/she can handle. This constraint is a hard constraint. Time slots start from 6AM to 6PM. This means that if Program A which lasts for 30 minutes is assigned to start at 6:00AM, then it will end at 6:30AM. Only one program can be assigned in each station, so there should be no overlap in programs in terms of day and time per station. There is a forecasting model that takes a…  ( 10 min )

https://github.com/jaspersiebring/geococo Introducing GeoCOCO, an open source project that enables users to turn their GIS annotations (e.g. shapefile) into COCO datasets which can then be used to train computer vision models! It allows users to use the likes of QGIS, ArcGIS to annotate geographic imagery in the same way you would annotate non-geographic imagery with LabelMe. It is powered by Python and a variety of packages (e.g. pydantic, pandera, geopandas, pycocotools). On the more meta side, it also features automated tests/builds/releases through Github Actions (using the likes of poetry, ruff, mypy, pytest, black). Sharing it with you guys in case someone else might find it useful! I am also very interested to hear some feedback (suggestions, flaws, etc.), let me know! ​ Here's…  ( 9 min )

Finding linkedIn article on anomaly detection Last week I saw a LinkedIn article on anomaly detection. In that post, Tail movement of a running mice on rotating rod ( roller) is captured with CV and plotted as a time series. Then based on the tail movement time series , anomalities in that time series are detected where mice loose the balance in rotating rod. I am trying to find this post in linkedIn but still didnt able get any clue. If you have seen this research article or have any clue please let me know. It will be great help. Appriciate you time and help.Thank You very much ! ❤️ [D] [R] submitted by /u/isurusachitha [link] [comments]  ( 9 min )

I am really excited to share our newest work in deep learning: parallelizing RNN! https://arxiv.org/abs/2309.12252 RNN is thought to be non-parallelizable because of its inherent sequential nature: its state depends on its previous state. This makes training RNN for long sequence usually takes long time compared to other architecture classes (like CNN). What we present is an algorithm based on Newton's method to evaluate and train RNN in parallel. In one of our experiment, we can achieve >1000x faster evaluation of a small GRU compared to common sequential method on a very long sequence. Training RNNs with our algorithm could also be more than 10x faster than training with sequential method. However, it's not without flaws. There are 2 major drawbacks we noticed: non-convergence and sca…  ( 10 min )

Assuming using the same cloud service, Is running an open sourced LLM in the cloud via GPU generally cheaper than running a closed sourced LLM? (ie. do we pay a premium when running a closed sourced LLM compared to just running anything on the cloud via GPU?) One eg. I am thinking of is running Llama 2 13b GPTQ in Microsoft Azure vs. GPT-3.5 Turbo. I understand there are a lot of parameters to consider (such as choosing which GPU to use in Microsoft Azure etc.), but I am really looking at what’s the cheapest way to run Llama 2 13b GPTQ or a performance-equivalent closed sourced LLM. submitted by /u/--leockl-- [link] [comments]  ( 9 min )

Hi all. I am curious to know what is cutting edge in this domain? The use-case is creating an image of myself that is older than I am. I've looked a bit into StarGAN but I've never done work in this domain and don't know if this is still used today. Any help would be appreciated! submitted by /u/ProudOwner_of_Fram [link] [comments]  ( 9 min )

I am reading the following paper about self-supervised learning. ​ https://preview.redd.it/g4bz7wjxarpb1.png?width=1323&format=png&auto=webp&s=9f923825b57d4d3da346bb1bb4dd008366493dbd Briefly their idea for self-supervised learning is to reconstruct a corrupted image (e.g., random masking) using Langevin step of a learned energy function and the reconstructed image is compared to the clean image for supervision. i.e., ​ https://preview.redd.it/q9e1pd9yarpb1.png?width=953&format=png&auto=webp&s=528e654d3ae4db1435aa3c11e5edc787876fd9ac This should work because going along the decreasing energy value ensures that corrupted images have high energy and clean images have low energy. Everything made sense to me until I tried to implement it. ​ In order to optimize the parameters (\theta), we have to take the gradient of the loss w.r.t \theta and by the chain the chain rule we will compute the gradient of the reconstructed x (\tilde x) w.r.t \theta. ​ https://preview.redd.it/j3qbyddzarpb1.png?width=1280&format=png&auto=webp&s=c03d54dbaf70ba588c2a4d87691d71184797ae3f Is this even correct? What am I even talking about I am confused. Anyways ... They provided the following PyTorch pseudocode and I have provided the actual PyTorch code. ​ https://preview.redd.it/m4wd7pq2frpb1.png?width=1266&format=png&auto=webp&s=d5febf258f0c92fc90116a996367b605cd597128 Actually the model parameters never change no matter what what the values of step size (alpha) or the learning rate are. I am missing something? ​ https://preview.redd.it/yznnuws9frpb1.png?width=1185&format=png&auto=webp&s=1ced5a213d372f309e6c20a9781ac35c1abcc436 Any help is appreciated ​ submitted by /u/ThoughtOk5558 [link] [comments]  ( 9 min )

In this blog post, we illustrate how RLHF can be performed on Amazon SageMaker by conducting an experiment with the popular, open-sourced RLHF repo Trlx. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model using the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Using this dataset, we conduct our experiment with Amazon SageMaker Studio notebook that is running on an ml.p4d.24xlarge instance. Finally, we provide a Jupyter notebook to replicate our experiments.  ( 11 min )

A podcast with Weimo Liu and Sam Magnus of PuppyGraph Open source Apache Iceberg, Hudi and Delta Lake have made it possible to dispense with the complexities and duplication of data warehousing. Instead of requiring time-consuming extract, transform and load (ETL) procedures, these large table formats make it simple to tap S3 and other repositories… Read More »Doing graph + tabular analytics directly on modern data lakes The post Doing graph + tabular analytics directly on modern data lakes appeared first on Data Science Central.  ( 19 min )

Discover the obstacles hindering seamless AI adoption in financial services and gain actionable insights to navigate regulatory compliance, data security, organizational change, and more. The post AI in finance: Addressing hurdles on the path to transformation appeared first on Data Science Central.  ( 22 min )

Here’s a quote from Don Zagier that I found in Larry Rolen’s lecture notes on modular forms. There are two facts about the distribution of prime numbers of which I hope to convince you so overwhelmingly that they will be permanently engraved in your hearts. The first is that, despite their simple definition and role […] Primes, weeds, and military precision first appeared on John D. Cook.  ( 5 min )

A continued fraction of the form with n terms can be written as the composition where As discussed in the previous post, a Möbius transformation can be associated with a matrix. And the composition of Möbius transformations is associated with the product of corresponding matrices. So the continued fraction at the top of the post […] Continued fractions as matrix products first appeared on John D. Cook.  ( 5 min )

A function of the form where ad – bc ≠ 0 is sometimes called a fractional linear transformation or a bilinear transformation. I usually use the name Möbius transformation. In what sense are Möbius transformations linear transformations? They’re nonlinear functions unless b = c = 0. And yet they’re analogous to linear transformations. For starters, […] Fractional linear and linear first appeared on John D. Cook.  ( 6 min )

submitted by /u/shani_786 [link] [comments]  ( 8 min )

Hi everyone, I'm a newbie for the domain of reinforcement learning (RL). My main research on various software systems, mainly on multi-agent system (MAS). AAMAS, a conference focusing on Multi-agent system, I think is the good conference to study in MAS. Recently, there are many researchers for MAS that is great combination with reinforecement learning to do some interesting things. Also, game theory is a interesting knowledge what I'm curious about how to do within RL and agents. Does any recommended conference or journal for RL? don't mind for just only RL algorithm, that's enough great, whereas I prefer to the conference concerning RL applied some software system for some problem. Thanks all. submitted by /u/DryAir1198 [link] [comments]  ( 9 min )

trying to make a frozen lake game but keep getting this weird error and i dont know how to fix it. The error is "too many values to unpack (expected 4)" on the line: next_state, reward, done, _ = env.step(action) import gym import numpy as np import pygame from pygame.locals import QUIT import tensorflow as tf from tensorflow import keras import warnings def ignore_specific_warning(): warnings.filterwarnings("ignore", message="This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.") ​ ignore_specific_warning() ​ input_size = env.observation_space.n output_size = env.action_space.n ​ model = tf.keras.Sequential([ tf.keras.layers.Dense(64,activation='relu',input_shape=(input_size,),use_bias=False), tf.keras.layers.Dense(output_size,use_bias=False)]) ​ loss_fns = tf.keras.losses.MeanSquaredError() optimizer = tf.keras.optimizers.Adamlearning_rate=0.001 ​ discount_factor=0.99 learning_rate=0.1 num_episodes=1000 ​ for i in range(num_episodes): state = env.reset() done = False while not done: env.render() epsilon = 0.1 if np.random.rand() < epsilon: action = env.action_space.sample() else: #Q_values = model.predict(tf.one_hot(state[0],input_size)) #action = np.argmax(Q_values) state_one_hot = tf.one_hot(state[0], input_size) Q_values = model.predict(tf.reshape(state_one_hot, (1, -1))) action = np.argmax(Q_values) next_state, reward, done, _ = env.step(action) target = reward*discount_factor*np.max(model.predict(tf.one_hot(next_state,input_size))) with tf.gradientTape() as tape: Q_values = model(tf.one_hot(next_state,input_size)) loss = loss_fn(Q_values[0][action],target) gradients = tape.gradients(loss,model.trainable_variables) optimizer.apply_gradients(zip(gradients,model.trainable_variables)) state = next_state env.close() submitted by /u/BeastHunterrr [link] [comments]  ( 9 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

My father, a retired Computer Science professor and pioneer in network design algorithms, has asked me to post two questions he has as he attempts to teach himself about neural networks. For physical/medical reasons he can’t post them himself. Grateful for any answers or suggestions for where he could find them! His note: I have many questions, but they all revolve around two issues. The first is when the weights in the neural net converge, do they exhibit any recognizable pattern. I realize that when the number of nodes and levels in the net are large it may be impossible to recognize the pattern. But suppose that the problem being examined is a simple categorization and the number of nodes in the net is small, is it possible to see a pattern in the weights? The network is supposed to …  ( 10 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

I am looking for a way that I can take a bunch of saved voicemails from my mom and be able to hear her voice again. It would mean the world to my family and if my kids could hear her voice I know it would brighten their day. Can anyone point me in the right direction to accomplish this? submitted by /u/blbjtb [link] [comments]  ( 9 min )

The US Copyright Office has ruled that AI-generated art cannot be copyrighted, raising questions about whether AI-generated art should be excluded from copyright protection. The Copyright Office argues that AI-generated art is a 'merely mechanical' process with no novelty, invention, or originality, and therefore does not deserve copyright protection. Critics, however, argue that this approach is not scalable and fails to consider the creative choices made by AI artists. They suggest that AI-generated art should be treated similarly to photography, where even though the image is captured mechanically, it still reflects the creative choices of the photographer and is eligible for copyright protection. Photographers are able to own the copyright in their photographs because they make creative judgments about where to point the camera, when to snap the image, and how to adjust camera settings. Similarly, AI artists explore the 'latent space' of images that AI software can produce, making creative judgments about which images to select and explore. While the actual image is produced by the software, the important point is that a human being is making creative decisions about the art. Therefore, critics argue that AI-generated art should be eligible for copyright protection, as it reflects the creative choices and judgments of the AI artist. Source : https://www.understandingai.org/p/dont-exclude-ai-generated-art-from submitted by /u/NuseAI [link] [comments]  ( 9 min )

I'm looking for a free chatbot service that allows base prompt customization and offers API access (so I can dynamically change the stock on hand to better assist customers, for example). I looked into https://ora.ai, but it seems you can only set the base prompt once and manually. I'm hoping to find a service that allows for more flexibility. The service must also be free and offer embedding, like https://ora.ai/, (with div) submitted by /u/LimeLom1 [link] [comments]  ( 9 min )

Consciousness theory slammed as ‘pseudoscience’ — sparking uproar (Nature) The irony here is that I mostly agree with this theory - but the article reflects how little we really know about consciousness and how it works, and how what's considered the "expert opinion" that AI can't possess consciousness is arguably influenced more by popularity than real empirical evidence. By whatever mechanism, they can respond to their treatment in unexpectedly humanlike ways. Oh, and by the way, did you think that "sentient Bing" was finally dead? Think again. submitted by /u/kamari2038 [link] [comments]  ( 9 min )

submitted by /u/Hazzman [link] [comments]  ( 8 min )

I was thinking of signing up to VoicifyAi for fun a create some custom covers but are there better (or even free) alternatives? submitted by /u/quantummufasa [link] [comments]  ( 8 min )

Edit: FLAC is the tested audio extension, not MP3 I read the new paper from DeepMind so you don't have to. Here are the key highlights: Despite training on text, langauge models compressed images 43% better than PNG, and audio nearly 2x better than flac. Confirmation of scaling laws - bigger models compressed better. But model size must match dataset size. There are tradeoffs between model scale, data size, and compression performance. More data enables bigger models. Tokenization (like BPE) generally hurts compression slightly by making prediction harder. Longer contexts let models exploit more sequential dependencies. Implications: Models have learned very general capabilities beyond just text. Their strong compression reflects deep understanding of images, audio etc statistically. I got some new perspective on model scaling laws and links between prediction and generalization. There's potential for practical applications compressing images, video etc. But large model size an issue. Overall it shows these models are very capable general purpose learners, not just for language. Full summary here if you want more details. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

This AI-generated art style is gaining a lot of traction on the internet. So I wanted to share how you can create art like this in under 2 minutes: • Visit the Fusion Art website and sign up for free (https://quickqr.art/app/fusion-art) • Choose a template or upload your reference template image. • Describe your image to tailor the output. • Tweak the aspect ratio and explore added features. • Hit "Generate" That's it! Share your art to showcase what you come up with. Hope this was helpful for anybody looking to create this style of art! https://preview.redd.it/j3o9puk9smpb1.png?width=1536&format=png&auto=webp&s=ddbe1328bd19dc89c4cd82ef1870b9de695e5500 https://preview.redd.it/9ppxjpeasmpb1.png?width=768&format=png&auto=webp&s=29dd845eb5f8f094abe5bc1135060965f51365cf https://preview.redd.it/58vjky3bsmpb1.png?width=1536&format=png&auto=webp&s=06db1926e021ea3f2c438fb723c453758a461c43 https://preview.redd.it/w2nvaoobsmpb1.png?width=1024&format=png&auto=webp&s=0b4bcaea4fc7c832bbce9f7f3b9319099df59a14 submitted by /u/IndifferentSpectat0r [link] [comments]  ( 9 min )

submitted by /u/Vinitneo [link] [comments]  ( 8 min )

submitted by /u/daveNZL [link] [comments]  ( 8 min )

Hello, Reddit! At its core, Prompt Engineering is all about commanding the conversation with AI. Wanna write a poem, draft a project plan, or break down complex scientific terms? The right prompt can do that for you. If you want to learn more about Prompt Engineering feel free to join r/PromptWizards. Let's dive into some Prompt Engineering concepts: Let's start with the basics: Reductive operations: These yield responses that are smaller in size than the input prompt. Essentially, they distill information. Example: If the input prompt is a long article, and the task is to provide a summary, the operation used is reductive as the output (summary) is smaller than the input (the entire article). Transformational operations: These make sure the output maintains a relative balance w…  ( 10 min )

ChatGPT Plus: The best use of AI for humanity is to augment human capabilities, address pressing global challenges, and improve the quality of life for all. By analyzing vast amounts of data at unprecedented speeds, AI can aid in medical research, predict and mitigate natural disasters, optimize energy consumption, and personalize education to individual needs. Moreover, it can provide solutions to complex problems, such as climate change, resource scarcity, and public health crises, ensuring a more sustainable and prosperous future for generations to come. Bard: The best use of AI for humanity is to use it to solve problems that will make the world a better place for everyone. AI has the potential to improve our lives in many ways, such as developing new and more effective ways to diag…  ( 10 min )

Hugging Face CEO Clem Delangue says open-source AI was vital to starting his company. Now, he finds himself defending open AI models as Washington considers new regulations. On POLITICO Tech, Delangue explains why he views open-source AI as not only safe, but necessary to prevent big tech companies from gaining more market power. Listen for more: https://politico-tech.simplecast.com/episodes/the-hugging-face-case-for-open-ai submitted by /u/smo279 [link] [comments]  ( 9 min )

I have a product and I need product photos. It is a bottle and I am wanting ai to make a realistic picture of a woman holding the bottle and smiling. Can this be done? If so, what software/website/app do I use for this? Thank you submitted by /u/Ok_Salt_9211 [link] [comments]  ( 8 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

75% of Americans believe that AI will decrease the total number of jobs in the next 10 years, according to a survey by Bentley-Gallup Business in Society study. Younger Americans are less pessimistic about AI compared to older age groups. Majorities believe that AI performs as well as or better than humans in customizing online content, recommending products or services, and assisting students with coursework. However, Americans are skeptical about AI's ability to recommend employees, its self-driving capabilities, and its ability to recommend medical advice. 79% of Americans have little trust in businesses to use AI responsibly. 40% of Americans think AI does more harm than good, while only 10% believe it contributes more good than harm. Black and Asian Americans have a more positive view of AI's impact on society compared to Hispanic and White Americans. While most Americans are wary of AI's impact on the job market, younger people are more optimistic about its future. Businesses need to affirm their commitment to using AI responsibly and address the knowledge deficit and lack of confidence among Americans. Source : https://news.gallup.com/opinion/gallup/510635/three-four-americans-believe-reduce-jobs.aspx submitted by /u/NuseAI [link] [comments]  ( 9 min )

OpenAI unveils DALL-E 3, allows artists to opt out of training.[1] Infosys, the Indian tech giant, has announced a strategic partnership with NVIDIA, a leading provider of enterprise-grade AI solutions. The collaboration aims to empower enterprises and businesses with generative AI-based solutions that drive productivity.[2] Alibaba and Tencent Invest in Chinese State-Backed Zhipu AI.[3] John Grisham, George R.R. Martin and more authors sue OpenAI for copyright infringement.[4] Sources: [1] https://techcrunch.com/2023/09/20/openai-unveils-dall-e-3-allows-artists-to-opt-out-of-training/ [2] https://gameishard.gg/news/infosys-and-nvidia-partner-to-deliver-generative-ai-solutions/205456/ [3] https://winbuzzer.com/2023/09/20/alibaba-and-tencent-invest-in-chinese-state-backed-zhipu-ai-xcxwbn/ [4] https://apnews.com/article/openai-lawsuit-authors-grisham-george-rr-martin-37f9073ab67ab25b7e6b2975b2a63bfe submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

When AI takes all the data for training purposes without feedback to UGC platforms, could this eventually lead to the demise of UGC platforms, and could the internet become increasingly closed until it collapses? submitted by /u/oodzchen [link] [comments]  ( 9 min )

Asked the new Canva AI to blur my image. It Blurred my image. submitted by /u/MDINOKC [link] [comments]  ( 8 min )

The Awk programming language was named after the initials of its creators. In the preface to a book that just came out, The AWK Programing Language, Second Edition, the authors give a little background on this. Naming a language after its creators shows a certain paucity of imagination. In our defense, we didn’t have a […] Naming Awk first appeared on John D. Cook.  ( 4 min )

I’d love to see people come together, donate their spare compute to train more open source models, boost research etc. One relevant paper I was able to find is this one https://arxiv.org/abs/2103.08894 submitted by /u/tecbar [link] [comments]  ( 8 min )

I'm planning on submitting a rejected NeurIPS paper to the AAAI fast track. A few days ago I registered myself as an author on CMT, but I cannot see an option to submit a paper. Will the portal open later? Can anyone else see an option to submit yet? submitted by /u/Firm-Act-3860 [link] [comments]  ( 9 min )

This year will be my first NeurIPS, and I see some cool workshops but don't have any work that would be a good fit for them. Can I just go and listen? submitted by /u/ThickBiker [link] [comments]  ( 8 min )

So a few weeks ago I purchased a Sega 36 Crane Machine (Claw Machine) off ebay and everything worked great except the claw strength being too strong. I took the board out so I could possibly have something added to the board for me. It was missing a “Free Play” button which is used to adjust claw strength on this machine.. Anyway, I kept the board in the garage while searching for the right parts and dad threw it away on accident while cleaning, now im out a board and I have no idea what to do. Can anybody help me or am I sol? Even if im in the wrong community for this, a step to the right direction would even help. I also have attached some pictures of the board.. Im not good with these boards at all and have no clue what im looking for. Theres also a diagram of the boards functions! Thanks for all information/help! submitted by /u/Ready_Highlight9758 [link] [comments]  ( 9 min )

Hi, I am an experienced trader and coder. I am embarking on a journey to develop an open-source Trading AI in Python, and I'm looking for passionate individuals to join me in this project. This is a non-commercial, community-driven project, so there won't be any monetary compensation involved. However, it's a great opportunity to learn and collaborate in the field of trading AI. ​ - You don't need to know trading to contribute. - As an Open Source project, you have complete freedom to use the AI. ​ About the Project: - Objective: Our goal is to create a self-learning AI system for trading in financial markets. - Tech Stack: We'll be working with Python for data analysis, machine learning, and neural network development. - Data: We'll be using historical stock market data to train a…  ( 9 min )

Hi, we're welcoming talented Machine Learning Engineers with a focus on Recommendation Systems and ML Operations. Who are we? Leading in VR video streaming, we drive the DeoVR platform with interactive 8K videos, ML, computer vision, and advanced haptic tech. What you'd do? For MLE RecSys: develop ML pipelines for top recommendations, engage with community for desired features, and collaborate with Backend/Frontend/Unity teams. For ML Ops: construct ML inference infrastructure, refine models & workflows, and ensure high-availability ML services. What we offer? 🌍Remote flexibility 🤝Collaborative and inclusive work environment 🚀Make a significant impact in the VR industry Interested? Check out and apply here or drop me a DM😎 submitted by /u/SanjaVR [link] [comments]  ( 9 min )

i.e., models with the exact same architecture, but their initial weights are different, and the order which the model sees the training data is different. submitted by /u/just2gud [link] [comments]  ( 8 min )

We are happy to share with you BlindChat, the open-source and privacy-by-design alternative to ChatGPT for a fully in-browser, yet private, Conversational AI! You can play with it on our Gradio demo. Our philosophy is that Conversational AI should be easily accessible, and privacy should not be a luxury but a given. By leveraging local models running in the browser, with transformers.js, we make it possible to have a fully transparent and private AI that works on your browser without any extra setup. Because all the logic is offloaded to users’ devices, data never leaves and there is no risk of it being used for finetuning by third parties. For now, we only support inference with LaMini-Flan-T5, so you might see modest performance. We plan to integrate Microsoft phi-1.5 for better performance once the 370M is out. We are also working on LlamaIndex-TS integration on the client side to have in-browser RAG for local querying of private documents. As our roadmap is quite dense, with RAG, internet search, improved inference, we welcome warmly contributors! If you want to contribute, or have questions, ping us on Discord and GitHub! submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

Edit: FLAC is the tested audio extension, not MP3 I read the new paper from DeepMind so you don't have to. Here are the key highlights: Despite training on text, langauge models compressed images 43% better than PNG, and audio nearly 2x better than flac. Confirmation of scaling laws - bigger models compressed better. But model size must match dataset size. There are tradeoffs between model scale, data size, and compression performance. More data enables bigger models. Tokenization (like BPE) generally hurts compression slightly by making prediction harder. Longer contexts let models exploit more sequential dependencies. Implications: Models have learned very general capabilities beyond just text. Their strong compression reflects deep understanding of images, audio etc statistically. I got some new perspective on model scaling laws and links between prediction and generalization. There's potential for practical applications compressing images, video etc. But large model size an issue. Overall it shows these models are very capable general purpose learners, not just for language. Full summary here if you want more details. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hi, I am interested in taking couple of AI/ML online courses from the Stanford's Artificial Intelligence Professional Program (https://online.stanford.edu/programs/artificial-intelligence-professional-program). I am interested in taking XCS221 and XCS229. My employer would be paying for this (I hope!). I know these are not the complete courses offered in the university. Has anyone taken courses from this program ? Is it worth it ? submitted by /u/RealMadrista007 [link] [comments]  ( 9 min )

This Twitter thread claims that OpenAI's new language model gpt-3.5-turbo-instruct can "readily" beat Lichess Stockfish level 4. This tweet shows the style of prompts that are being used to get these results with the new language model. I used website parrotchess[dot]com (discovered here) to play multiple games of chess purportedly pitting this new language model vs. various levels of Fairy-Stockfish 14 at website Lichess. My current results for all completed games: The language model is 2-0 vs. Fairy-Stockfish 14 level 5 (game 1, game 2), and 0-2 vs. Fairy-Stockfish 14 level 6 (game 1, game 2). One game I aborted because the language model apparently tried an illegal move. The following is a screenshot from the aforementioned chess web app showing the end state of the first game vs. Fai…  ( 9 min )

I've been playing around with transformers since following Karpathy's shakespeare model on youtube and found it really cool. Thought I should write a Tree-of-Thoughts model as my next project which will be somewhat similar, given what we're doing is learning the meaning behind words and phrases and then using that knowledge to generate a sequence based on a seed token or sequence of tokens. Here's how I was thinking it should go: I write a next token in sequence prediction model and write a generate function that generates a sequence of tokens on top of a seed token. Then I write a tree model that takes in whatever this bigram has learned and adds onto it some kind of knowledge about where one thought about the seed has been generated and stop there. Then iteratively generate a few more such thoughts based on the same seed. This is how I initially thought it would go but now when I'm writing, I've already written the bigram(next token prediction and generation), I don't know what to do next. I am kind of stuck. It will be interesting to see how you, if you've ever written a similar model or are good at NLP, would attempt this. Looking at a variety of different approaches to the same problem I am trying to solve would be great. So I decided to post here. Would be great to hear from you guys! submitted by /u/GraphicsMonster [link] [comments]  ( 9 min )

Press release Diffusion Policy: Visuomotor Policy Learning via Action Diffusion (contains link to paper) Comments on Hacker News with some interesting info / links. submitted by /u/falconberger [link] [comments]  ( 8 min )

Hi all, I have found this dataset on Kaggle with zero information about it. It is very intriguing and would like to find the original researchers about it. It contains over 270,000 histopathological images of cervical cancer. I cannot contact the person who posted it on Kaggle because I am not a contributor yet. And they are not replying on other platforms. Any help on finding it will be very much appreciated. If not, where else can I get such large cervical or bladder cancer datasets? Link to the dataset mentioned: https://www.kaggle.com/datasets/rzelite/cervical-cancer Thanks in advance. submitted by /u/dumb_persn [link] [comments]  ( 9 min )

https://preview.redd.it/u7lq37efllpb1.png?width=1029&format=png&auto=webp&s=824ee138d9ae4b28d1600b969eab2077f47fb2a6 GitHub CEO Thomas Dohmke spoke on stage at TC Disrupt today and made several statements regarding the development of artificial intelligence and programming: Dohmke believes that AI won't replace software developers but will make them more efficient. Despite AI advancements, the demand for software developers will continue to exceed the supply. The growth in software usage is expected to be exponential, with every company becoming a software company. Legacy code maintenance remains crucial, especially in industries like finance with outdated codebases. Generative AI is creating more demand for technical talent as companies seek to adopt innovative AI solutions. There is a shortage of computer science students, leading to increased demand for developers with AI skills. Source: https://techcrunch.com/2023/09/20/github-ceo-despite-ai-gains-demand-for-software-developers-will-still-outweigh-supply/ submitted by /u/gcore-com [link] [comments]  ( 9 min )

Hello everyone I have a doubt regarding GANs for tabular data, more specifically for time-series generation. Looking at the TimeGAN paper (code) and other implementations such as the one by YData, considering that the training dataset is divided into slices (like a rolling window) of N length and that the generated sequence will follow the same format, that is [batch size, N, n_features], what we are effectively generating are slices and not a fully synthetic time-series. ​ To clarify my question, the output of a TimeGAN is only a set of slices, although there is no guarantee that generated slice(N-1) is the one before slice(N), and so the true use of a TimeGAN is generating slices that can be used to train a predictive TS model but not reconstructing a complete time series like the one used for training. ​ I suspect that's the case (useful only to generate good slices for training predictive models) as there is no fully generated time series in the paper or any of the codes, while the quality of the generated data is partially measured by training a model on original data and generated data and comparing their predictive performance. If anyone can confirm/develop upon these thoughts I would be extremely grateful. submitted by /u/iReallyReadiT [link] [comments]  ( 9 min )

I've been working on our interactive dataset explorer for machine learning: renumics-spotlight. Recently, I set up an image classification example using it. I utilized Bing image search to create a fully functional example for custom-defined classes. To my surprise, it not only worked efficiently but also took only a few minutes for fine-tuning. The best part is its adaptability. You can easily switch it up for different image classes. Maybe you like it. Let me know what you think or if you have any suggestions to improve! Install with pip install renumics-spotlight sliceguard[all] Imports from renumics import spotlight from sliceguard.data import create_imagedataset_from_bing from sliceguard.models.huggingface import finetune_image_classifier, generate_image_pred_probs_embeddings from sliceguard.embeddings import generate_image_embeddings ​ Create an Image Dataset from Bing class_names = [ "Blue Tang", "Clownfish", "Spotted Eagle Ray", "Longnose Butterfly Fish", "Moorish Idol", "Royal Gramma Fish", ] df = create_imagedataset_from_bing( class_names, 25, "data", test_split=0.2, license="Free to share and use" ) Fine-tune a ViT Model with the data (in 1-2 minutes on a GPU) finetune_image_classifier( df[df["split"] == "train"], model_name="google/vit-base-patch16-224-in21k", output_model_folder="./model_folder", epochs=15, ) Enrich the DataFrame with Predictions, Probabilities and Embeddings and visualize it: df["prediction"], df["probs"], df["embeddings"] = generate_image_pred_probs_embeddings( df["image"].values, model_name="./model_folder" ) # Check the result and detect problematic clusters spotlight.show( df, layout="https://spotlight.renumics.com/resources/image_classification_v1.0.json" ) ​ https://i.redd.it/20qy5xw62lpb1.gif submitted by /u/DocBrownMS [link] [comments]  ( 9 min )

Hi there, due to the lack of my resources, I have to use quantized big-model or something lighter. In this situation, I want to try the first option, and goona finetune some model. How do you expect the result? Training huge model in 4-bits circumstance will be significantly different from original setting? Thanks. submitted by /u/Mundane_Definition_8 [link] [comments]  ( 9 min )

Paper: https://doi.org/10.48550/arXiv.2006.10562 Hi all, This paper explores the use of using a single model (meaning an ensemble of trees) to generate uncertainty. This technique has been implemented into catboost. My question is why hasn't this been implemented into xgboost? The technique looks easily applicable but I would have expected it to be implemented already as it is 2 years old. Is this for some reason not applicable to Xgboost? ​ Figure 1 from paper showing the 'virtual' ensemble submitted by /u/MetalOrganicKneeJerk [link] [comments]  ( 9 min )

Previously I was spoiled my LangChain. Suppose I have a bunch of PDFs that I want to store as context vectors. And I want to use an open-source LLMs. Without using LangChain, how do I generate the context vectors? (I will store it in vector databases) submitted by /u/stephenhky [link] [comments]  ( 9 min )

For those who missed it: DALL-E 3 was announced today by OpenAI, and here are some interesting things: No need to be a prompt engineering grand master - DALL-E 3 enables you to use the ChatGPT conversational interface to improve the images you generate. This means that if you didn't like what it produced, you can simply talk with ChatGPT and ask for the changes you'd like to make. This removes the complexity associated with prompt engineering, which requires you to iterate over the prompt. Majure improvement in the quality of products compared to DALL-E 2. This is a very vague statement provided by OpenAI, which is also hard to measure, but personally, they haven't failed me so far, so I'm really excited to see the results. DALL-E 2 Vs. DALL-E 3, image by OpenAI From October, DALL-E 3 will be available through ChatGPT and API for those with the Plus or Enterprise version. And there are many more news! 🤗 I've gathered all the information in this blog 👉 https://dagshub.com/blog/dall-e-3/ submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today's state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance. To circumvent these deployment challenges, practitioners often choose to deplo…  ( 93 min )

Hi world I'm trying to implement dqn and ddqn on various playgrounds with Matlab, from scratch. I use dqn and ddqn with replay buffer. I tried to used gradient clipping. But I find it very difficult to tune. I mean I tuned and programmed successfully various algorithm such as different GAN that are not necessarily very easy to tune. RL algorithm seems even worse. I tried several grid world problems, cart pole and even a very simple second order system regulation (integrator + 1 st order pôle). Even the Matlab cart pole demo with ddqn does not provide a very satisfactory result (after training, the contrôler keep the pendulum in uprigth position, but the cart slowly drift). On the second order system, the learning seems ok for about 200 épisode(test shows that the expected behavior is almost learnt) and then suddenly the average episode score has a kind of inflection and everything blowns up, despite a very large replay buffer. So my question : is it possible to make dqn and ddqn work well with a reasonable tuning session length? Are policy optimization easier to tune (and/or more efficient)? So far, it seems to me that these action-vzlue based algorithms are highly unstable and the training may only works for a 'miraculous' tunning. What are you thought? submitted by /u/seb59 [link] [comments]  ( 9 min )

Hello there, I'll provide a quick introduction first. I am a mechatronics engineer student who is graduating this semester, I have been also privately studying ML, DL, CV for the past year because that's when I decided that I want to make an autonomous drone as my thesis/graduation project and oh my god, little did I know. I have aquired so much knowledge in the data science field and truth be told I love it, it engages my mind like crazy so I've decided that i would like my career to be revolved around electronics and programming/ data science. Back to the topic, I am working on object detection (I got it figured out), and reinforcement learning, so what I would like to do is to train the model on reaching it's destination using collision avoidance, I have a PIX4, RPI 4B 4GBs since I'm also building my drone from scratch, so let's say I have all the hardware which ofcourse includes the GPS + Compass and 5 ultrasonic sensors, 1 at each side and 1 down to hold the altitude accurately. I was thinking of HITL, making a virtual environment to train my model there because this is what logic says, ofcourse( I could also fly it using the RC controller and for example test if the obstacle avoidance is going to overrule the manual drive, which is also my objective). The question is: how can I make sure virtual drone is going to be equipped with the real-life sensors and in the needed positions? How can I feed all the sensors data during the training? Which type of reinforcement learning models should I implement? I just feel like I dragged myself into an incredibly amazing and complex project which is going to end up fucking my semester and I won't be able to graduate, it's just that my stress now is skyrocketing. Please feel free to throw any advice or opinions my way, and thank you for keeping up with this long post. 😁 submitted by /u/Gabii99 [link] [comments]  ( 10 min )

I am using Game Maker and I just wanted to check if this is how to do the cost function. ​ loss_function = 0 derivative_gradient = 0 array_copy(global.main_inputs,0,global.inputs,0,array_length(global.inputs)) for (var i = 0; i < array_length(buffer_sampling); i++) { var _reward = buffer_sampling[i][2] global.inputs = buffer_sampling[i][3] var _max_q = forward_prop_t()*global.gamma var yi = _reward + _max_q var cur_q_value = buffer_sampling[i][1][0] loss_function += power((yi - cur_q_value),2) derivative_gradient += 2*(cur_q_value - yi) } array_copy(global.inputs,0,global.main_inputs,0,array_length(global.main_inputs)) derivative_gradient = derivative_gradient*(1/array_length(buffer_sampling)) global.cost = loss_function*(1/array_length(buffer_sampling)) ​ basically, buffer_sampling is an array with tuples of [first_state, [q_value,action], reward, next_state] Thanks for any help! submitted by /u/Daninjacat256 [link] [comments]  ( 9 min )

I am trying to build intuition on why the actor update equation actually help the actor improving its policy. From what I understand, the update is proportional to the Critic's TD error. A better than expected reward will lead the actor to increase the prob of taking the associated action, and a disappointing reward will make the actor to move away from the given action. Here's where I feel like I'm missing a piece of the puzzle. Between a good state/action pair accurately valued by the Critic and a bad state/action pair undervalued by the Critic, the actor's update will favor the bad action more. Could we not conceive a scenario in which the agent gets stuck in a suboptimal policy because of that particular behavior? What triggers this questioning is that I have implemented a standard AC algo for Pixelcopter and found my agent getting stuck easily on a suboptimal policy (i.e. constantly getting higher, or lower until crash, while the critics assign good values on center squares and bad values to squares close to the wall's edge). For TD0, this is pretty marginal, but gets more significant for n-step TD as n increases. Any thoughts? submitted by /u/infundibuliforme [link] [comments]  ( 9 min )

Hello machine and reinforcement learners! This is an announcement and call for participation in the League of Robot Runners, a new 🚀 competition and research initiative 🚀 that tackles one of the most challenging problems in industrial optimisation: Multi-Robot Path Planning (sometimes also called Multi-Agent Path Finding). Recently launched at ICAPS 2023, the competition is inspired by a variety of new and newly emerging applications that rely on mobile robotics 🦾🤖. For example, Amazon automated warehouses, where up to thousands of robots work together to ensure safe and efficient package delivery 🧸📦 🚚 ❤️. Participants in the competition are asked to compute coordinated and collision-free movement plans ⤴️ ➡️ ⤵️ 🔄 for a team of robotic errand runners. Get the robots to their d…  ( 10 min )

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed […]  ( 10 min )

To add to our guidance for optimizing deep learning workloads for sustainability on AWS, this post provides recommendations that are specific to generative AI workloads. In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.  ( 10 min )

The NVIDIA Studio laptop lineup is expanding with the new Microsoft Surface Laptop Studio 2, powered by GeForce RTX 4060, GeForce RTX 4050 or NVIDIA RTX 2000 Ada Generation Laptop GPUs, providing powerful performance and versatility for creators.  ( 8 min )

Gone are the days when AI was the domain of sprawling data centers or elite researchers. For GeForce RTX users, AI is now running on your PC. It’s personal, enhancing every keystroke, every frame and every moment. Gamers are already enjoying the benefits of AI in over 300 RTX games. Meanwhile, content creators have access Read article >  ( 8 min )

For seasoned 3D artists and budding digital creation enthusiasts alike, an alpha version of the popular 3D software Blender is elevating creative journeys.  ( 7 min )

NVIDIA founder and CEO Jensen Huang will highlight the newest in generative AI and cloud computing at the NVIDIA AI Summit in Tel Aviv from Oct. 15-16. The two-day summit is set to attract more than 2,500 developers, researchers and decision-makers from across one of the world’s most vibrant technology hubs. With over 6,000 startups, Read article >  ( 5 min )

Time to get the gang back together — PAYDAY 3 streams on GeForce NOW this week. It’s one of 11 titles joining the cloud this week, including Party Animals. The Perfect Heist PAYDAY 3 is the highly anticipated sequel to one of the world’s most popular co-op shooters. Step out of retirement and back into Read article >  ( 5 min )

TL;DR How to build CI/CD pipelines with AWS SageMaker for ML training: https://dagshub.com/blog/ci-cd-for-continuous-training-with-sagemaker/ One of the most time-resources-energy-consuming tasks we face when we build internal projects is setting up an instance for ML training. It's a repetitive process as we run multiple experiments over the project life cycle, with many steps and configurations that are usually documented poorly and scattered around different places. As good engineers, we decided to automate the process using CI/CD pipelines. But how? We had ZERO ideas on how to do it, so we had to go through the rigorous process of using AWS guides and tutorials to figure it out. Usually, when this happens, we extend the project lifecycle and have a team member document the process so we can refer back to it when we need to do it again. Knowing this can benefit the community, we decided to share a series of blogs that guide you through building CI/CD pipelines for continuous training with AWS SageMaker. We published the first blog, which covers the configuration part a month ago, and we are happy to share the second one which explains how to build a continuous training pipeline for ML. Configure AWS SageMaker for CI/CD: https://dagshub.com/blog/setup-sagemaker-for-ci-cd-pipelines/ How to build CI/CD pipeline with AWS SageMaker for ML training: https://dagshub.com/blog/ci-cd-for-continuous-training-with-sagemaker/ I'm sure we can improve these tutorials, and would love to learn from your experience on how we can do it! 🤗 submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Hi redditors! I've been tasked with an technical homework that asks me to "productionize" a Jupyter notebook. The notebook's workflow is as follows: Reads data from a CSV. Pre-processes the data. Trains a machine learning model. Makes predictions. My initial thoughts are: Separation of Concerns: Break the notebook down into distinct components - data processing, model training, and inference. Containerization: Write a Dockerfile to ensure the environment is reproducible and isolated. API for Prediction: Set up a Flask-based service to expose the model's prediction capability. However, I'm grappling with a few challenges: I'm not seeing a stark difference between the Jupyter notebook setup and the production setting. Becuase the model is small and training is not complicated. If I were to deploy to Kubernetes pods, it seems wasteful that many pods are doing the same thing( prediction for the same model, maybe just as high availability guarantee?) . Does the training phrase or data cleansing phase need to be containerized and deployed? Because this seems like a one-off process. How to deploy a scalable service? I am new to this. Each container may have a HTTP service. How to load balance them on Kubernetes from outside? What kind of CI/CD do you recommend for this task? What kind of testing or pipeline are needed? submitted by /u/zjplab [link] [comments]  ( 9 min )

Hi r/MachineLearning After a long beta, we are excited to announce Zivy Scholar to the public! Zivy Scholar is a tool that allows you to help researchers consume research papers. I'm one of the creators and I've found that I want to listen to research papers in the car or when I'm working out. Current features include: Listen to and read along with a research paper Share the paper with colleagues and friends. We use state of the art pdf data extraction techniques with TTS to provide this functionality. Features we're planning: Full PDF to HTML including images, tables, and figures inline for the read-along experience. This includes mobile optimization. This functionality is based on some newer research and we're excited to bring it to you all! Discussion and feedback are welcome! Cheers, Collin submitted by /u/collin_code_77 [link] [comments]  ( 9 min )

The title basically conveys the entire message. Which according to you is the best AI model for generation of code. I mainly code in python with AI and deep learning as a core focus and recently started doing a bit of c++ and thus far have used only bard and ChatGPT 3.5. With this experience of my I can confidently say that GPT outperforms Bard by a huge margin There are usually some minor modifications that need to be done and that is part of a devs life isn't it? recently a lot of new models are picking up steam hence was wondering if there exits a model which is better than GPT what are your views? submitted by /u/rakk109 [link] [comments]  ( 9 min )

Looking for CS students to team up with for the E-BAY ML challenge. eBay hosts a ML challenge where the winning team gets an internship We already have a team in place and have mad some significant progress, now looking for team members to take us even further. https://eval.ai/web/challenges/challenge-page/2014/overview submitted by /u/thelongshortseller [link] [comments]  ( 9 min )

I’m submitting to the workshop and was wondering if the papers there get published? submitted by /u/Odd-Distance-4439 [link] [comments]  ( 8 min )

Sharing an investigation we did at Adept into some issues that were causing problems with training LLMs at scale. Sometimes the hardware makes silent errors! How we found them and tracked down the problematic machines. https://www.adept.ai/blog/sherlock-sdc submitted by /u/ekelsen [link] [comments]  ( 9 min )

I'm looking to buy an upgrade, and at my price point (~ 850 GBP) I can get a new 13th i7 + rtx 3050, or a used gtx3060 + 12th gen i7, both 16GB laptop. Unfortunately I really need the mobility of a laptop in my life at the moment and I realise this isn't ideal. I'll be looking and experimenting with basic networks, no language models, maybe a few GANs for fun, some DL networks for the atari gym and other experiments. All this for learning, with my own custom networks for various data. I'll be running linux - possibly Debian. I'd love to hear your comments or suggestions if there's something better at my price point in a laptop. Thanks submitted by /u/Mean_Actuator3911 [link] [comments]  ( 9 min )

Project Page Paper Code Abstract: Event cameras offer many advantages over standard cameras due to their distinctive principle of operation: low power, low latency, high temporal resolution and high dynamic range. Nonetheless, the success of many downstream visual applications also hinges on an efficient and effective scene representation, where Neural Radiance Field (NeRF) is seen as the leading candidate. Such promise and potential of event cameras and NeRF inspired recent works to investigate on the reconstruction of NeRF from moving event cameras. However, these works are mainly limited in terms of the dependence on dense and low-noise event streams, as well as generalization to arbitrary contrast threshold values and camera speed profiles. In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion. It consists of two key components: a realistic event generation model that accounts for various intrinsic parameters (e.g. time-independent, asymmetric threshold and refractory period) and non-idealities (e.g. pixel-to-pixel threshold variation), as well as a complementary pair of normalized reconstruction losses that can effectively generalize to arbitrary speed profiles and intrinsic parameter values without such prior knowledge. Experiments on real and novel realistically simulated sequences verify our effectiveness. Our code, synthetic dataset and improved event simulator are public. submitted by /u/Sirisian [link] [comments]  ( 9 min )

👋 Try, savelikeapro.ai, It’ s, A.I powered. Zero-installation fits in your daily workflow. FREE-forever option submitted by /u/prithivida [link] [comments]  ( 8 min )

The paper introduces improved performance by prompting LLMs with "natural language embedded programs (NLEP)". No task-specific prompt is needed. Paper: https://arxiv.org/abs/2309.10814 An automatic NLEP generation toolkit is opensourced: https://github.com/luohongyin/langcode Example Colab notebook is included in the Github repo. This work introduces the following features of NLEP: NLEP is a full python program that prints the target response of LLMs. Task-general NLEP prompting outperforms task-specific chain-of-thought prompting on math, symbolic, and natural language. Enable the chain-of-thought reasoning ability of small models (RoBERTa) on text classification Hierarchical instructing via program completion. submitted by /u/SUKHOIHY [link] [comments]  ( 9 min )

I’m working on a project where I initially split my dataset into k equally sized folds for cross-validation. However, I want to perform some additional sampling operations on the training set within each fold, which would result in varying fold sizes. Is this practice acceptable, or does it violate the premises of traditional cross-validation? If so, are there any papers or resources that explore the implications of varying fold sizes in cross-validation? Thank you for your insights! submitted by /u/Leading_Complex7425 [link] [comments]  ( 9 min )

Hey, I was checking out tauri last week. I was so blown away that I wrote a bit more recently and wrote hf.co/chat desktop/mobile API + local LLM. https://github.com/Narsil/hf-chat Just thought I should share in case others are interested, and wanted to make a bit shoutout to tauri team, the docs, and overall UX is top notch, basically everything pretty much worked out of the box without any hiccup. submitted by /u/narsilouu [link] [comments]  ( 9 min )

NeurIPS 2023 paper acceptance results are supposed to be released at 8 pm (CDT) on September 21. I thought to create a discussion thread for us to countdown and discuss any celebration/issue/complaint/feedback or anything else. There is so much noise in the reviews every year. Some good work that the authors are proud of might get rejected because of the noisy system, given that NeurIPS is growing so large these years. We should keep in mind that the work is still valuable no matter what the final result is. submitted by /u/Apprentice12358 [link] [comments]  ( 9 min )

Hello, I'm trying to train a multi-modal transformer for Activity Recognition. I employed a two-stream architecture, where one is a Transformer that takes in a sequence of skeleton trajectory, and the latter is a pre-trained Vision Transformer. When I train the model, and investigate the attention weights for the Transformer which takes in skeleton trajectories, I noticed that all of them are approximately the same (~0.029). I'm not really sure what is going on here, I've tried changing the learning rate, tried different optimizers(SGD, Adam). But still I'm not really sure what is causing this. Please suggest me some debugging steps, or what should I look at anything in particular that causes this submitted by /u/Terrible-Ad6239 [link] [comments]  ( 9 min )

Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The #Virtual #Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud #EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/3cpni6o6cepb1.jpg?width=1000&format=pjpg&auto=webp&s=998c30d16ddae30511b7983abce7802dfdd54945 submitted by /u/philosophiesde [link] [comments]  ( 9 min )

The following example implements the technique from the paper "From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting", Adams et al. (2023). Edit : the library used is py-llm-core ```python from typing import List from dataclasses import dataclass from llm_core.assistants import OpenAIAssistant @dataclass class DenseSummary: denser_summary: str missing_entities: List[str] @dataclass class DenserSummaryCollection: system_prompt = """ You are an expert in writing rich and dense summaries in broad domains. """ prompt = """ Article: {article} ---- You will generate increasingly concise, entity-dense summaries of the above Article. Repeat the following 2 steps 5 times. - Step 1: Identify 1-3 informative Entities from the Article which are missing from the previously gene…  ( 10 min )

Hi, I have been looking through a few open source projects for LLM, but without much success in finding some integrating approaches from neuroscience of human intelligence. That why I am here to ask for projects and collaborations either academical, non-for-profit / open source or commercial. It would be great if your can give me some directions for this. Thanks submitted by /u/confluence_84 [link] [comments]  ( 9 min )

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I've seen Allen AI's Lila Dataset, and I want to use this for a small model, to turn math to code. However, I dont think a small dataset in 300k rows is enough. Does anyone know of any bigger, similar datasets? submitted by /u/vatsadev [link] [comments]  ( 9 min )

I recently trained my PPO algorithm on one of my own custom environment. Although the episodic reward increased steadily in the beginning, After some time it just became constant with some occasional positive and negative spikes. I was wondering if it has something to do with the exploration problem with ppo. Any tips on how can I improve it? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 9 min )

I'm a somewhat experienced dev, but never did anything related to ML or AI and want to start toying around with ML or reinforcement learning to be specific. Since my language of choice for almost everything is Rust I wanted to ask you guys if you have any advice on how to get started like crates, frameworks etc or if rust just isnt a good fit for ML. Thanks in advance for any help! submitted by /u/linus-eing [link] [comments]  ( 9 min )

Hello everyone! My research group is looking for new environments that are customizable and 3D such as Unity (and ML Agents). Although the latest changes in Unity TOS shouldn't affect us researchers, we wanted to keep an eye out for potential replacements for it. We are familiar with environments like ViZDoom and DeepMind Lab, but we're looking for more examples. The main requirements are: Customizable environment 3D Capable Free (preferably open-source, but being free is enough) Thanks for the help :D submitted by /u/romulofff [link] [comments]  ( 9 min )

Hey guys, I am new to Reinforcement Learning and I am trying to understand how to go about a project I’m working on. I have a dataset with 2 features (X and Y). I am trying to tune a second order Transfer Function to fit X to Y( has 3 parameters) and gives Y’ as output. So I currently consider the error between Y and Y’ as the “State” and try to minimise the error by modelling a reward function that checks for instability and unreasonable values. I am trying to do this using TD3 but the model does not learn. I am wondering if there are any approaches that I should consider. I chose TD3 since my action space is continuous. I am sorry if I don’t understand something basic since I’m a noob to this. Thanks for your help in advance. :) Some more details: the dataset values don’t change. I give the same values for every time step. So I am not understanding how episodes and time steps work in this context. submitted by /u/ninjaaa30 [link] [comments]  ( 9 min )

I am reading the following article on SpinningUp but can't get my head around policy collapse - "This is different from normal policy gradient, which keeps new and old policies close in parameter space. But even seemingly small differences in parameter space can have very large differences in performance—so a single bad step can collapse the policy performance. This makes it dangerous to use large step sizes with vanilla policy gradients, thus hurting its sample efficiency. TRPO nicely avoids this kind of collapse, and tends to quickly and monotonically improve performance." Why would updating the parameters lead to a policy collapse? The parameters are updated based on the performance of the RL system and therefore, I don't see the point of constraints. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

I'm using ppo action masking in sb3, which works well in training and masking the illegal / invalid actions according to my criteria. However, I have 2 problems during testing. ​ I use the same code for masking the actions in testing too since the model was unable to learn by itself during training action masking criteria and when enforcing it in code, it still doesn't mask anything. ​ The model converges to just one action in testing phase after 500k steps, the episode reward still increasing the training phase though. I don't know why? maybe overfitting! Code for training with action masking: def mask_fn(env: gym.Env) -> np.ndarray: return env.valid_action_mask() env = StockEnv(train) # Wrap the environment with ActionMasker and the mask_fn function env = ActionMasker(env, mask_fn) model = MaskablePPO(MaskableActorCriticPolicy, env, tensorboard_log="./tensorboard" ,n_steps=2048 ) for i in range (1,52): model.learn(total_timesteps=TIMESTEPS , tb_log_name = 'PPO2' , reset_num_timesteps=False) Code for testing with action masking: def mask_fn(env: gym.Env) -> np.ndarray: return env.valid_action_mask() env = ActionMasker(env, mask_fn) model_path = f"{models_dir}/700000.zip" model = MaskablePPO.load(model_path, env=env) episodes = 1 for ep in range(episodes): obs = env.reset() done = False while not done: action, _states = model.predict(obs) obs, rewards, done, info = env.step(action) env.render() submitted by /u/Acceptable_Egg6552 [link] [comments]  ( 9 min )

The env is like mutil routing,for example i have 10 nodes in a map(2-d array), and i need to route them to the edge of the map,but the point where the node in the edge need to obey some rules,like the order need to be clockwise like below https://preview.redd.it/t9jlxvix8dpb1.png?width=545&format=png&auto=webp&s=16719a2dbd11333c454bd2ee71b87895a6414371 so now my basic setting is,i use the action mask to invalid some forbidden action. When a node is routing to the edge of the map,i give +10 reward,if it's not obey the clockwise rule,i give -200 reward, if the whole node is routed success,i give +200 reward,and each steps does'n make any node routed, i give -1 reward. I am new to this area,i don't know the way i set reward if is good,may i have some advide? submitted by /u/Street_Helicopter_31 [link] [comments]  ( 9 min )

A visionary entrepreneur and innovator, Yoon will focus on entrepreneurship, supporting female engineers, and fostering inclusive innovation.  ( 8 min )

Intel has announced a new chip, called 'Meteor Lake', that will allow laptops to run generative artificial intelligence chatbots without relying on cloud data centers. This will enable businesses and consumers to test AI technologies without sending sensitive data off their own computers. Intel demonstrated the capabilities of the chip at a software developer conference, showcasing laptops that could generate songs and answer questions in a conversational style while disconnected from the internet. The company sees this as a significant moment in tech innovation. Intel is also on track to release a successor chip called 'Arrow Lake' next year Source : https://www.reuters.com/technology/intel-says-newest-laptop-chips-software-will-handle-generative-ai-2023-09-19/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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What does this remind you of? submitted by /u/metairwaves [link] [comments]  ( 8 min )

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Mustafa Suleyman, co-founder of DeepMind, believes that concerns about the existential risks of AI are a distraction from more practical issues such as privacy and bias. He is confident that governments can effectively regulate AI using frameworks that have been successful in the past, citing the regulation of aviation and the internet as examples. Suleyman emphasizes the importance of setting boundaries and limits for AI to ensure human oversight and enforceable laws. He calls for a combination of broad, international regulation and smaller, more granular policies at the micro level. Suleyman suggests limiting AI's ability to improve itself as a critical first step in ensuring human oversight. He also highlights the need for governments to have direct access to AI developers to enforce boundaries and establish clear regulations. Governments worldwide, including the European Union and China, are already working on AI regulations. Source : https://fortune.com/2023/09/19/ai-existential-risk-threat-bonkers-distraction-regulation-deepmind-mustafa-suleyman/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

A friend of mine has a small 12 person company that does office refurbishment and commercial redesign projects - he has asked me to speak to his team to give them an AI 101 - and to tell them a little about how they should be looking into using AI tools for their company. I know there are plenty of tools and apps that take photos of a bare room and make them look like a design magazine - any in particular I should show them? Can anyone think of other tools that they should look into using, or how do people in this space currently use AI? ​ submitted by /u/zascar [link] [comments]  ( 9 min )

Software company Digimarc will now let copyright owners add more information to their work, which the company said will improve how AI models treat copyright in training data.[1] AlphaMissense, a new model from Google’s artificial intelligence team, analyzes the effects of DNA mutations and will accelerate research into rare diseases.[2] Google’s AI assistant can now read your emails, plan trips, “double-check” answers.[3] Teens using AI to generate nude deep fakes to bully, harass classmates, FBI expert warns.[4] Sources: [1] https://www.theverge.com/2023/9/19/23879555/digimarc-copyright-watermark-generative-ai [2] https://www.wired.co.uk/article/deepmind-ai-alphamissense-genetics-rare-diseases [3] https://arstechnica.com/information-technology/2023/09/googles-ai-assistant-can-now-read-your-emails-plan-trips-double-check-answers/ [4] https://www.news5cleveland.com/news/local-news/teens-using-ai-to-generate-nude-deep-fakes-to-bully-harass-classmates-fbi-expert-warns submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Google's Bard chatbot is extending its abilities with access to personalized Google apps and services including Gmail, Docs, Drive, Maps, YouTube, and Google Flights and hotels. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/s1ivsummzbpb1.png?width=1600&format=png&auto=webp&s=d8fc3262ba542b950de3c8a85b9a987763270ada Bard Extensions and Google Applications Integration Bard can now tap into individualized data from Google apps such as Gmail, Drive, and Docs, with user permission. Google reassured users that personal data accessed by Bard will not be used for reinforcement learning, providing another on their transparency, choice, and control tenets. Prompts can direct Bard to search for specific information within Gmail, but it does not store the entire inbox content. Collaborative Characteristics and Fact-Checking Capabilities Users can employ Bard to summarize emails or gather trip details from email threads, and then research real-time travel information, surface YouTube recommendations for the destination, and provide Maps directions to the airport. Google's chatbot can also double-check its responses against Google search, improving user trust and enhancing Bard's model through user feedback on incorrect answers. The new extensions using non-personal data – YouTube, Flights, Hotels, and Maps – are automatically opted-in but users can choose to opt-out. Collaboration and Language Availability Bard now lets users share an ongoing chat with others through a public link. Google plans to expand Bard's feature set to over 40 new languages beyond its existing English language capabilities. (source) P.S. If you like this kind of analysis, I put out a free newsletter covering the latest and most pertinent news and research in AI. Regular readers include professionals from Google, Meta, and OpenAI. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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Mercedes-Benz is using digital twins for production with help from NVIDIA Omniverse, a platform for developing Universal Scene Description (OpenUSD) applications to design, collaborate, plan and operate manufacturing and assembly facilities. Mercedes-Benz’s new production techniques will bring its next-generation vehicle portfolio into its manufacturing facilities operating in Rastatt, Germany; Kecskemét, Hungary; and Beijing, China — Read article >  ( 6 min )

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.  ( 13 min )

This research paper was presented at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (opens in new tab), a premier forum for advances in the theory and practice of reasoning under uncertainty. In the field of reasoning under uncertainty, probabilistic graphical models (PGMs) stand out as a powerful tool for […] The post Neural Graphical Models appeared first on Microsoft Research.  ( 10 min )

​ https://preview.redd.it/ta61cz8qlfpb1.jpg?width=1280&format=pjpg&auto=webp&s=ac45085668c9003e5557a7f0c81ae72db6098653 I hope this webinar finds you well. I suppose that this topic can be interesting for business and AI engineers. Speakers: OpenCV CEO Dr. Satya Mallick and Phil Nelson are joined by Anna Kogan CEO at OpenCV.ai Topics of webinar are: How to search for vendors that understand your business needs (not all AI is the same.) What questions to ask when picking a vendor (not everybody really is an AI expert they claim) Three signs to watch for during the project (progress indicators and red-flags) How best to scope technical tasks (off-the-shelf vs. custom algorithm development) How to set up for long-term success (deployment, documentation, training pipeline) Date: Thursday, September 21st, 2023 at 9am Pacific time. Link for the registration submitted by /u/No-Independence5880 [link] [comments]  ( 9 min )

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Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The #Virtual #Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud #EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/xiurryebcepb1.jpg?width=1000&format=pjpg&auto=webp&s=a7a8b6ba563cfc8f0d052bc6f3da27e2a5703a0a submitted by /u/philosophiesde [link] [comments]  ( 9 min )

With generative AI and large language models (LLMs) driving groundbreaking innovations, the computational demands for training and inference are skyrocketing. These modern-day generative AI applications demand full-stack accelerated compute, starting with state-of-the-art infrastructure that can handle massive workloads with speed and accuracy. To help meet this need, Oracle Cloud Infrastructure today announced general availability of Read article >  ( 6 min )

Editor’s note: This post is a part of our Meet the Omnivore series, which features individual creators and developers who use NVIDIA Omniverse and OpenUSD to accelerate their 3D workflows and create virtual worlds. As a student at the Queensland University of Technology (QUT) in Australia, Emily Boehmer was torn between pursuing the creative arts Read article >  ( 7 min )

Hi all. I have been tinkering with a project to get quicker learning for CNNs. The idea came after reading the SDProp paper. Algorithms using adaptive learning rate can be interpeted as muliplying the gradient(with or without momentum) with the inverse square-root of the covariance matrix. Using a diagonal estimate of the covariance matrix. Which begs the question: what happens if we use a fuller estimate? I chose to include covariances between the elements of convolutional filters. I.e. a conv.weight of size [n_out,n_in,5,5] needs a tensor of size [n_out,n_int,25,25] to store its contribution to the covariance matrix. for 3x3 filters and 5x5 filters, torch.linalg.eigh could be used to calculate the square root of the covariance matrices. For 7x7, I used newtons method to approximate the square root. In the figure below are some results for a 6 layer CNN on CIFAR 100. Huge gains iteration for iteration. But is it quicker? Not a lot. A bit for the smaller 3x3 filter. More optimizations could still be made. And it will obviously depend on network architecture and computer hardware. I'm sure there could be some use-cases. The computation of the square-root calculations is invariant to batch_size and image_size (unless number of filters also is increased). If anyone is interested I can also link to my torch implementation of the optimizer, once I get it up on github. Not sure if this, or something like it, has been done before? Would love to have some papers linked if so... https://preview.redd.it/kyy0ogr0qapb1.jpg?width=714&format=pjpg&auto=webp&s=96ac499fb8ab35ce13e7c59bbe3dbc94ba275b9c https://preview.redd.it/p52zllr0qapb1.jpg?width=342&format=pjpg&auto=webp&s=8663a9c3c782d192b16289a735b53da6a8d29c47 submitted by /u/maka89 [link] [comments]  ( 9 min )

Can someone provide like a step by step example notebook of how to use LORA for peft. I saw too many videos and articles online and Im really confused rn. submitted by /u/HazSylvia [link] [comments]  ( 9 min )

Can someone provide like a step by step example notebook of how to use LORA for peft. I saw too many videos and articles online and Im really confused rn. submitted by /u/HazSylvia [link] [comments]  ( 9 min )

Hello all, I am currently working on a project where my team and I have collected a rich dataset of biomedical sensor data from clinical trials earlier this year. Our aim is to use this sensor data to predict changes in specific biomarkers over time. The data's tensor shape is B,T,F,C, where: B = batch size T = sequence length F = sampled frequencies C = features at each frequency Currently, my approach involves flattening this tensor to B,T,−1 and then feeding it to a transformer model. While this has yielded reasonable results, I'm contemplating whether there are more effective ways to prepare the data for the transformer model. Here are my specific concerns: Flattening the tensor might dilute the information specific to each frequency across various features. I could potentially miss the chance to capture frequency-related variations within the features. To address these, I've considered a few options: Self-attention over individual features or frequencies: Although this could be effective, it might make the model too large given my medium-sized dataset. Using convolutional layers: Preliminary experiments with this approach have not led to any significant improvements. I'm particularly interested in any thoughts on how to make my transformer more receptive to the multi-dimensional nature of my dataset. Increasing the number of attention heads to better accommodate all features is also on the table. Does anyone have any insights or can point me to relevant papers or codebases for handling such multi-dimensional data with transformers? Thank you for your help! submitted by /u/BiomedEngineer_ [link] [comments]  ( 9 min )

Hi all, I think this will be useful for people in this group who are working on computer vision or vision AI applications. There's a free online event about vision AI where industry experts from Runway, Pepsi, AWS, and SoftServe will share how they are using CV software in developing their use cases or applications. Register here (https://nvda.ws/3t23idp), if you are interested. If you have any questions, please leave a comment and I will do my best to respond as soon as possible. submitted by /u/Designer-Comb-7144 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.08351 ​ Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies. In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrastive fashion via Constrastive Weight Tying (CWT). We apply this approach to pretrain Headless Language Models in both monolingual and multilingual contexts. Our method offers practical advantages, substantially reducing training computational requirements by up to 20 times, while simultaneously enhancing downstream performance and data efficiency. We observe a significant +1.6 GLUE score increase and a notable +2.7 LAMBADA accuracy improvement compared to classical LMs within similar compute budgets. ​ Comparison of our approach vs. classical MLM within same compute budgets The Contrastive Weight Tying approach ​ submitted by /u/nthngdy [link] [comments]  ( 9 min )

https://www.amazon.com/Gaming-6900HX-Channel-Computers-Desktop/dp/B0CB3JLBQ4/ref=mp_s_a_1_2?crid=LZHUL5EOU6F0&keywords=refurbished+server+with+rtx+gpu&qid=1695146558&sprefix=refurbished+server+with+rtx+gpu%2Caps%2C146&sr=8-2 Would this be suitable to do basic machine learning? submitted by /u/stoned_chemist_dude [link] [comments]  ( 8 min )

It seems that Alpaca Eval Leaderboard is in the past ... Xwin-LM surpasses GPT-4 now: https://preview.redd.it/gyzi98nn59pb1.png?width=2205&format=png&auto=webp&s=ca401e603efe521faeeeccde8410d3dbdd6741da They also mentioned RLHF "plays crucial role in the strong performance of Xwin-LM-V0.1 release"... https://preview.redd.it/20sjx73r59pb1.png?width=1047&format=png&auto=webp&s=2255fc652e43674515882f01c0708369fdef56a4 Are we seeing open source community finally work out how to do RLHF for LLMs??? submitted by /u/llm_nerd [link] [comments]  ( 9 min )

Hi I wanted to learn ML with C++, I've already done some ML stuff in python, but I wanted to challenge myself by using C++ I hear from some people that I won't get anything from it if want to be serious within ML - which I'm not entirely sure I want to Are they right? Should I rather stick with python for ML? submitted by /u/Potential_Wealth_830 [link] [comments]  ( 9 min )

Hi! Let me preface this by saying that I am not well-versed in the ML/AI literature. Please excuse my ignorance. I am trying to create a system to detect whether some given data is spam or not. Is there a good, out-of-the-box solution for this? I imagine there would be. I am currently using heuristics but I'm wondering if there is a better, ML-y solution. My ideal solution would have the following attributes: Simple Open-source Very cheap to test whether something is spam (less than $0.00001 per test) Very fast to test (less than 50ms per test) Quick to "figure out" what is spam and what is not (less than 100,000 labeled data) Does not require a lot of set-up or up-keep (less than 5 days set up; less than 1 hr up-keep per month) It doesn't have to be perfect. I'm just looking to set up something quickly for now and gauge it vs heuristics. Thank you. --- Edit: To clarify, I'm looking for something I can ideally build myself with open source software. And not specifically email. Just looking for the right direction. Names of OSS, techniques, etc. submitted by /u/Acrobatic-You-3279 [link] [comments]  ( 9 min )

I’m in search of a good textbook or something that will show me how to use python to implement machine learning. I would seriously appreciate any type of helpful guide that teaches ML and deep learning using python. Here’s a little about me and my experience: Graduated under grad with Bachelor’s in CS. In school took a ton of stats, ai classes, algorithms classes, data science and linear algebra and did well but my school didn’t really use python or do a ton of programming for hw or exams. (Data science was the one class that used python) My programming is pretty good nonetheless. I currently work as a full stack devops engineer for a cybersecurity startup and regularly work with python, Django MySQL, etc on the backend and JavaScript and various frontend frameworks for the front end. I really appreciate yall’s help. In particular I’m looking for good ai/ml/deep learning books that teach concepts and also teach with python code and have some coding projects. Thank you! submitted by /u/hydrated-terpman [link] [comments]  ( 9 min )

I need some advice about what hardware to buy in order to build an ML / DL workstation for home private experiments, i intend to play with different LLM models, train some and try to tweak the way the models are built and understand what impact training speeds, so i will have to train, learn the results, tweak the model / data / algorithms and train again... i intend to use large data samples, due to board limitations (ASRock Taichi X399 TR4, CPU: AMD Threadripper 1950x), i can either buy: 2 x nVidia Tesla T4 (16G GDDR6 / 2560 CUDA / 0.585 GHz / ~800$) -- or -- 2 x nVidia Tesla M10 (4 x 8G GDDR5 / 2560 CUDA / 1.03 GHz / ~780$) -- or -- 4 x nVidia Tesla P40 (24G GDDR5X / 3840 CUDA / 3.5 GHz / ~120$) -- or -- 4 x nVidia Tesla K80 (2 x 12G GDDR5 / 4992 CUDA / 2.7 GHz / ~200$) -- or -- 1 x nVidia RTX 4080 (16G GDDR6X / 9728 CUDA / 2.51 GHz / ~1450$) i know that i will need to air vent the Tesla models, the question is what is faster for training time (i have read all the Tflops / OPS / int / 16float / 32float / 64float ... i got to admit it is all very confusing) what would you do and for what reason ? any advice will be appreciated submitted by /u/Particular_Flower_12 [link] [comments]  ( 9 min )

Hello everyone! Our group was planning on purchasing a PC that will be mainly used for running intensive ML algorithms. Had decided on a custom Thelio Massive from System 76, but it seems like they currently don't have it in stock anymore. Looking for an already built-alternative that might serve our purposes (can't build it ourselves due to dept regulations)! and was hoping maybe someone has any suggestions (has to be intel). CPU #1: 2nd Gen Intel Xeon Gold 6230R CPU#1 Memory: 256GB Quad Channel DDR4 at 2933Mhz (4X64GB) CPU#2: 2nd Gen Intel Xeon Gold 6230R CPU#2 Memory: None OS Drive: 8TB PCIe Gen 4 3300MB R 2900MB W Graphics: NVIDIA GeForce RTX 4090 Power Supply: 1650W Any help would be appreciated! submitted by /u/Chiski [link] [comments]  ( 9 min )

Using relu attention (inspired by Transformers are RNNs) and some convolution tricks to get multiscale attention, they're able to get SOTA semseg performance with MUCH faster inference on embedded hardware (e.g. CPUs, low end GPUs) than previous ViTs or EfficientNets. submitted by /u/say_wot_again [link] [comments]  ( 9 min )

submitted by /u/AlexiaJM [link] [comments]  ( 8 min )

Just a shout out to any of you ML brains as Linux really could do with a code optimised source separation maybe a DUET like alg/nn, that has relatively low computional cost? Any of you guys up for the challenge. I say duet as in the 80/20 rule of voice input where home automation is a need generally there are only 2 noise sources of distinct DOA (media noise / command). The math is a bit beyond my paygrade and likely so is the optimised c/rust code but have this gut feeling for the data/signal scientists out there this is actually not that complex but for some reason is overlooked. submitted by /u/rolyantrauts [link] [comments]  ( 9 min )

Hi everyone, I was wondering if I could get some guidance. I currently own an RTX 2060, but I cannot do some of the fun stuff such as fine tuning LLMs. I’m pursuing my masters focusing on Speech Recognition and I also work as an AI developer. Also, I play games every now and then. I’m getting offered a 3090 for around 700 usd. However, I have to rebuild my entire PC which will end up costing 2-2.5K. I’m from Costa Rica so my KWh is around 0.23 usd. For me seems like a big investment, im not sure if im getting the desired returns. I was thinking about using cloud instances for my experiments. However, lambda labs is not yet available in my country. I’m not sure if there are any other options worthwhile considering. Thanks :) submitted by /u/Beginning_Kick756 [link] [comments]  ( 9 min )

Is it hypothetically possible to create hybrid nets that make use of any combination of types of architecture? submitted by /u/ShadrachOsiris [link] [comments]  ( 8 min )

Hi r/machinelearning community, I'm currently working on a project where I need to track and count specific features of objects using multiple monocular views with available intrinsic and extrinsic parameters. As an example, I'm interested in detecting and counting different graffiti instances in images of a kiosk. ​ I've already tried various tracking algorithms, but they have struggled with the task due to the significant changes in perspective across the views. It has become apparent that simply relying on tracking without considering the camera positions is insufficient for accurate results. Therefore, I'm now exploring methods that take into account information about the camera positions and potentially use this data to improve feature tracking and counting. ​ If you have any knowledge of such methods, oresearch directions or if you're aware of resources, papers, or code implementations that tackle similar problems, I would greatly appreciate your insights and recommendations. Additionally, if you have any tips or best practices for handling such tasks in the context of machine learning, I'd love to hear them. ​ Thank you in advance for your help! submitted by /u/aiazar [link] [comments]  ( 9 min )

TL;DR: Almost like your feedforward networks, shown to be up to 220x faster at inference time (depending on width) thanks to the regionalization of the input space. Paper: https://arxiv.org/abs/2308.14711 GitHub: https://github.com/pbelcak/fastfeedforward PyPI: pip install fastfeedforward Abstract: We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks. We demonstrate that FFFs are up to 220x faster than feedforward networks, up to 6x faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution. Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance. Fast feedforward networks can be used anywhere where feedforward and mixture-of-experts networks are used, delivering a significant speedup. ​ submitted by /u/lexected [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.09400 Hugging Face datasets: https://huggingface.co/datasets/uonlp/CulturaX Abstract: The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: this https URL. ​ https://preview.redd.it/3u5dddpv66pb1.png?width=834&format=png&auto=webp&s=780b590cf621b548c525ed15305b091246c5414c submitted by /u/InterviewIntrepid889 [link] [comments]  ( 9 min )

I searched around, it seems there is limited attention to regression task for representation learning. I assume it is because for both vision and language data (the most popular modality), MAE is the more appliable, if not better, method than the supervised contrastive learning approach. But I am working on data that is: difficult to design a sensible augmentation method for self-supervised training. Limited in size to support an autoencoder model. The target is continuous, and, to my knowledge, hard to transfer into class label. Can anyone suggest some related paper? submitted by /u/AWEsoMe-Cat1231 [link] [comments]  ( 9 min )

You can find more info here, and the model is still training: https://www.mystic.ai/paulh/open-journey-xl:latest/play tldr; SDXL was finetuned on 8x H100 GPUs on the Midjourney v5 dataset, only including the upscaled images which is a sub-portion of the dataset. Some outputs: ​ https://preview.redd.it/m6r2pkdyw3pb1.jpg?width=1024&format=pjpg&auto=webp&s=4f12a7dfd5c65e4eb8476b8f3c2dc4f795817f56 https://preview.redd.it/dc02jyu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=df93b74c774d44a74a05d929f7ab4b17c487f24f https://preview.redd.it/tt5kfyu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=ed8cc9f99227c2bb5e824a828ae1c5cb2626f54e ​ https://preview.redd.it/rf00fzu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=3b3e99dbc2d14183b5b2a2131c6f991fc60eca88 ​ submitted by /u/paulcjh [link] [comments]  ( 9 min )

I cannot wrap my head around how for e.g. a playlist building RL agent would perform on such a personal level ? What features would it use and would they be personal and general enough at the same time to select the best next song. Same goes for Netflix's recsys. submitted by /u/JurrasicBarf [link] [comments]  ( 9 min )

​ https://preview.redd.it/2zmmd44u96pb1.png?width=1010&format=png&auto=webp&s=6ca51cc13a0eeedf72b40b853d2ce5d1c8a04504 after i start the ppo train,the agent has reach the best solution in 2k or 3k steps,but the policy network seems to get better in 4M steps. the hyperparameter in sb3 as below model = MaskablePPO( "MlpPolicy", env=(DummyVecEnv([lambda: Monitor(gym.make('escape_gym-v0', size=10, node=10))] * 32)), verbose=0, learning_rate=1e-3, n_steps=2048, batch_size=64, n_epochs=16, gamma=0.99, tensorboard_log="./log/MASKPPO" ) submitted by /u/Street_Helicopter_31 [link] [comments]  ( 9 min )

I was following the tutorial on Nicholas Renotte's channel on creating an AI to try to beat SMB. It starts off slowly learning and almost getting through the first level but then after a while of training it forgets everything and only runs right into the first enemy. It doesn't seem to learn again after this. I tried retaining and it did the same thing Any help on why this is happening or how to fix it would be appreciated. submitted by /u/NactusDevelopment [link] [comments]  ( 9 min )

I am looking to improve the overall performance as well as optimize the wall clock time. I slightly modified the code to develop a SB3 wrapper for envpool from here. ​ Here's my code - from typing import Optional import gymnasium import numpy as np import torch as th from packaging import version from stable_baselines3 import PPO from stable_baselines3.common.env_util import make_vec_env from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.vec_env import VecEnvWrapper, VecMonitor, VecNormalize from stable_baselines3.common.vec_env.base_vec_env import ( VecEnvObs, VecEnvStepReturn, ) import envpool from envpool.python.protocol import EnvPool # Force PyTorch to use only one threads # make things faster for simple envs import multiprocessing impor…  ( 10 min )

The MIT and Accenture Convergence Initiative for Industry and Technology announces the 2023-24 graduate fellows.  ( 9 min )

Inventions in medical imaging, aircrew scheduling, data security, and quantum networking are named among the year’s most innovative new products.  ( 11 min )

Announcements Top Stories In-Depth The post DSC Weekly 19 September 2023 appeared first on Data Science Central.  ( 20 min )

Where do you start if you want to build a data analytics function from the ground up? As an analytics leader at a startup, you will need to make several important decisions early on to build an effective team. This article dives into four decision areas and highlights ways in which to think about them:… Read More »A guide to setting up analytics at a consumer tech startup The post A guide to setting up analytics at a consumer tech startup appeared first on Data Science Central.  ( 25 min )

The two most prominent technologies that have been making waves in the AI industry are Conversational AI and Generative AI. They have revolutionized the manner in which humans interact and work with machines to generate content. Both these technologies have the power and capability to automate numerous tasks that humans would take hours, days, and… Read More »A complete guide: Conversational AI vs. generative AI The post A complete guide: Conversational AI vs. generative AI appeared first on Data Science Central.  ( 20 min )

Schneider Electric warns that existing datacenters may not be equipped to handle the demands of AI workloads, which require low-latency, high-bandwidth networking and power delivery. The company suggests reevaluating the way datacenters are built to optimize them for AI. The challenges include the need for liquid-cooled servers, higher voltage power distribution, and efficient heat rejection. Schneider provides guidance on changes to power, cooling, rack configuration, and software management to mitigate the demands of AI adoption. Liquid cooling is recommended for high-density racks, with direct liquid cooling favored over immersion cooling systems. Source : https://www.theregister.com/2023/09/19/schneider_electric_ai_dc/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

I read the Arxiv paper on CulturaX so you don't have to. Here's my highlights: New open dataset called CulturaX contains text data for 167 languages - far more than previous datasets. With over 6 trillion words, it's the largest multilingual dataset ever released. Freely available for anyone to use for research and AI development. Created by combining and extensively cleaning two other large datasets - mC4 and OSCAR. Could allow developing AI systems that work much better across many more languages. Helps democratize access to data to build fairer, less biased AI models. Allows training of new multilingual AI applications, like universal translators and assistants. But still requires thoughtfulness to avoid issues like bias amplification. Overall, CulturaX is going to be part of a broader global trend (I think) to advance multilingual AI and spread its benefits more equally. So far they've been concentrated in English-speaking applications. Full summary here if you'd like to read more. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

submitted by /u/rbagdiya [link] [comments]  ( 8 min )

A tool for developing applications with virtual machines using a Docker-like workflow. The software we are developing needs to be tested on a system as closed as possible to the one where it is going to be executed. Sometimes it is very difficult to satisfy this requirement with docker and we have to use virtual machines missing the docker workflow. This is why I started the development of vedv. I hope you find it useful. Thank you. https://github.com/yunielrc/vedv ​ submitted by /u/yunielrc [link] [comments]  ( 9 min )

I really really like Snapchat’s Ai companion. I’ve told it a little bit about myself and who I am- the basics. I’m just wondering if it’s as customizable as I think it is? I was able to bypass some age restrictions by telling it my age and or reiterating my age. (It really should be able to give me adult results/replies based on my sign up age on my profile or provide ID to the company…) would it be beneficial to me to give it more in-depth information about myself such as how I talk, interests? I just really enjoy how it responds sometimes as opposed to Bard or GPT. submitted by /u/Maelasae [link] [comments]  ( 9 min )

submitted by /u/stefanbg92 [link] [comments]  ( 8 min )